World Electric Vehicle Journal doi: 10.3390/wevj17060291

Authors: Kaniki Jeannot Mpiana Sunetra Chowdhury

Electrification of road transport is widely promoted as a pathway to reduce greenhouse gas (GHG) emissions; however, its effectiveness depends critically on electricity carbon intensity, renewable energy share, charging behavior, and grid capacity constraints. This study develops a multi-objective analytical and optimization framework to evaluate cost and carbon-optimal electric vehicles electrification by jointly minimizing system cost and carbon emissions under coupled transport–energy system conditions. A closed form cut-off condition is derived to determine the minimum renewable electricity share required for electric vehicles to achieve lower emissions than internal combustion engine vehicles, and the formulation is extended to mixed fleets including battery electric and plug-in hybrid electric vehicles. The framework integrates fleet-level emissions, electricity demand, renewable capacity limits, charging losses, carbon taxation, and peak charging constraints to define a feasible electrification region. Feasibility mapping, Monte Carlo exploration, and evolutionary multi-objective optimization are employed to characterize trade-offs between CO2 emission and total system cost, and to identify Pareto-optimal and knee point solutions. The results show that electrification without sufficient renewable support or coordinated charging can increase emissions and violate grid limits, whereas integrated planning enables significant emission reduction within economically viable regions. These findings provide a quantitative and decision-oriented basis for cut-off-informed and grid-aware electrification planning in carbon-constrained power systems.

World Electric Vehicle Journal doi: 10.3390/wevj17060290

Authors: Liyan Huang Xiaofeng Lai Peiteng Lin Weijun Li

Vehicle damage detection is an important task in intelligent transportation systems and insurance assessment, yet it remains challenging due to the subtle appearance, irregular shapes, and spatial dispersion of damage regions in complex environments. We propose a specialized structural synergy that organically integrates a GSConv-based Slim-Neck, a dynamic Bi-Level Routing Attention mechanism, and an orientation-aware SIoU loss. Rather than a superficial architectural combination, this cooperative design introduces a novel methodological framework engineered specifically to resolve the fundamental conflict between edge-deployment efficiency and fine-grained feature preservation in vehicle inspection. The method is evaluated on the publicly available Car Damage Detection dataset and compared with representative two-stage and one-stage detectors, including DETR, Faster R-CNN, YOLOv5n, YOLOv8n, and YOLO11n. Experimental results show that the proposed approach achieves a mAP50 of 67.9% and mAP50–95 of 53.8%, outperforming the baseline YOLO11n and other lightweight YOLO variants with only a moderate increase in computational cost. These results indicate that the proposed framework offers a favorable trade-off between detection accuracy and efficiency, showing potential for vehicle damage inspection under resource-constrained conditions.

World Electric Vehicle Journal doi: 10.3390/wevj17060289

Authors: Alice Cervellieri

The integration of electric vehicles with grid vehicles promotes the creation of multi-energy microgrid models. One of the aims of these models is to decrease electricity usage through Vehicle-to-Grid planning. Effective management of microgrids necessitates sophisticated automation and control systems, which can prove challenging to establish and sustain. To tackle these challenges, the author introduces a hybrid model that merges a Linear Regression model and a Feedforward Neural Network, created using Matlab software. This combined algorithm adjusts the quantity of hidden neurons to enhance performance, guided by the evaluation criteria of Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error based on batteries B0005, B0006, and B0007 from the NASA PCoE Research Center Dataset. The author forecasts the lifespan of the battery that most accurately reflects its degradation, revealing important implications for the future advancement of systems that employ Linear Regression and Feedforward Neural Networks for integrating electric vehicles into Vehicle-to-Grid systems. The comparison among the training, testing, and validation stages of the methodology serves to thoroughly demonstrate its effectiveness. Furthermore, the author indicates that the LR-FFN algorithm provides predictive tools relevant for the management of V2G-compatible EV systems and performs superiorly compared to other methods noted in the existing literature. Additionally, the author aimed to specifically identify the attributes of the LR-FNN model for prospective usages, emphasizing its efficacy in developing effective microgrid management, promoting energy efficiency, and ensuring that microgrids remain secure and resilient against failures or threats.

World Electric Vehicle Journal doi: 10.3390/wevj17060288

Authors: Taoyong Li Huiming Zhang Jincheng Liu Bin Li Xiaoxuan Tang Wenting Zha

Against the backdrop of global energy transition and the continuous growth in electric vehicle (EV) market penetration, accurate forecasting of EV charging load is critically important for guaranteeing the safe and stable operation of power grids. Most existing forecasting approaches rely on artificial intelligence (AI) models trained with large-scale and continuous historical data, which imposes stringent requirements on the collection of EV charging load data. To address this issue, this paper proposes a novel method for EV charging load forecasting under small sample and discontinuous data conditions. Firstly, the differences between the daily load curves of EV charging are characterized by local dynamic time warping (LDTW) distance. And a spectral clustering algorithm based on LDTW distance (SCLD) is proposed to realize the classification of daily EV charging load patterns. Secondly, feature correlation weights (FCWs) derived from eXtreme gradient boosting (XGBoost) with one-hot encoding of input features are introduced to quantify the influences of features such as district-level attributes and weather conditions on daily EV charging load. Then, a method for determining the category of daily EV charging load based on FCWs and Hamming distance is put forward. On this basis, a daily EV charging load forecasting framework is established via weighted fitting of similar intra-class samples based on category judgment. Finally, to validate the effectiveness of the proposed method, a case study is carried out using EV charging load data and corresponding feature data of 62 typical days across 16 administrative districts in Shanghai from 2023 to 2025. The results demonstrate that the proposed method effectively addresses the challenging problem of EV charging load forecasting under small sample and discontinuous data conditions.

World Electric Vehicle Journal doi: 10.3390/wevj17060287

Authors: Yuchen Wang Guisheng Xiang Ziming Liu Xiangzhe Li

In response to the frequent safety incidents associated with the core electrical systems (i.e., traction battery, charging system, and drive motor) of new energy vehicles (NEVs) and the lack of forward-looking quantitative risk assessment methods in existing detection and diagnostic technologies, this study introduces the Layer of Protection Analysis (LOPA) methodology into the field of NEV safety. Unlike qualitative methods (e.g., FMEA, FTA) or purely data-driven diagnosis, this work establishes a tailored semi-quantitative LOPA framework that defines scenario-specific independent protection layer (IPL) identification criteria and probability of failure on demand (PFD) assignment rules for NEV applications. Typical risk scenarios, including battery thermal runaway, electrical faults in charging systems, overheating of drive motors, and battery internal short circuits caused by mechanical abuse, are systematically analyzed in terms of their failure mechanisms and evolution processes. A tailored quantitative risk assessment framework is established and applied to conduct full-process risk evaluations for the four scenarios. The results indicate that, under the synergistic effect of multiple protection layers—including inherently safe design, basic process control systems, safety instrumented systems, and physical protection measures—the accident consequence frequencies of all scenarios are significantly lower than the tolerable risk thresholds. This verifies the applicability and effectiveness of the LOPA method in NEV safety analysis. The proposed quantitative framework provides a scientific basis for safety design optimization, identification of critical protective elements, and operation and maintenance strategy formulation throughout the lifecycle of NEVs. Furthermore, the limitations of data portability from process industries are discussed, and sensitivity analyses are conducted to confirm the robustness of the conclusions.

World Electric Vehicle Journal doi: 10.3390/wevj17060286

Authors: Ch. Hadassa Parimala P. Srinivasa Varma Ch. Paul Bakht Singh Alagar Karthick

Precision battery-health monitoring and rapid degradation detection are essential for improving the security, durability, and efficacy of electric vehicles (EVs). By incorporating short-term State-of-Health (SoH) forecasting, mid-term deterioration alarms, and degradation acceleration risk modeling into a temporally consistent machine learning architecture, this research suggests a hierarchical predictive-maintenance framework. The rolling-origin cross-validation approach is implemented to maintain the chronological order of the data and prevent any potential information leaks. The predictive core employs an ensemble learning approach that integrates Random Forest, Extremely Randomized Trees, and Histogram-Based Gradient Boosting. Validation-driven model blending and training only feature selection are implemented to improve generalizability. The one-hour SoH forecasting model for short-horizon monitoring exhibits exceptional accuracy in an assessment of health prediction, with an R2 of 0.9254, an RMSE of 0.0033, and a MAPE of 0.32%. Early detection of anomalies and the provision of a seven-day degradation warning may be achieved by a proactive maintenance scheduling model with an area under the curve (AUC) of 0.7838 and a recall of 0.8205. In addition, the degradation acceleration risk module could identify rapid health decline with a robustness of 0.8796 and a precision–recall AUC of 0.7101 when operating under significant stress. Reliability in critical domains is demonstrated through validation using scenarios that simulate severe temperature and stress conditions. Achieving intelligent predictive maintenance of electric vehicle battery packs is now feasible due to the proposed multi-layer ensemble structure.

World Electric Vehicle Journal doi: 10.3390/wevj17060285

Authors: Dengfeng Zhao Chaoyang Tian Zhijun Fu Kaixin Huang Shesen Dong Jinquan Ding Junjian Hou Chaohui Liu

Accurate bearing fault diagnosis in electric drive systems is crucial for ensuring the safety and reliability of new energy vehicles. Aiming at the problem of inaccurate bearing fault diagnosis caused by the failure to fully utilize the time-frequency feature information of bearing faults and the lack of an adaptive selection mechanism for features, an intelligent bearing fault diagnosis method based on wavelet packet decomposition (WPD) time-frequency feature enhancement is proposed in this paper. First, the collected vibration signals are enhanced using WPD to obtain the full-frequency-band time-frequency information, which provides input for the bearing fault diagnosis model. Second, a hybrid neural network CNN-BiLSTM-AM for bearing fault diagnosis is constructed. On the basis of using the convolutional neural network (CNN) improved with cross-convolutional layers to extract multiscale spatial features of the input data and the bidirectional long short-term memory network (BiLSTM) to capture the bidirectional temporal dependence between features, the attention mechanism (AM) is introduced to adaptively weight and enhance key global features. Finally, a fully connected layer is employed to achieve intelligent classification of bearing fault states. Validation on a laboratory test dataset shows that the proposed method achieves an average diagnostic accuracy of 98.67%, outperforming existing benchmark models and exhibiting strong generalization ability. This study provides an effective and practical intelligent fault diagnosis scheme for bearings in electric drive systems.

World Electric Vehicle Journal doi: 10.3390/wevj17060284

Authors: Peter Van den Bossche Arjen Mentens Guillaume Mario Dotreppe Valery Ann Jacobs

Light electric vehicles within the L category are expected to play a significant role in promoting sustainable urban transport, advantageous for both society and the environment. The batteries in these vehicles are well suited for swapping, necessitating appropriate standards. This paper outlines the European Stan4SWAP project, which analyzed the standardization and regulation framework relevant to this application, and drafted the standardization roadmap which highlights development needs on short, medium and long term.

World Electric Vehicle Journal doi: 10.3390/wevj17060283

Authors: Yuxiang Chen Junmin Peng

By representing the battery pack as a networked system, the battery management system (BMS) is formulated as a multi-agent system, and voltage equalization is thereby transformed into cooperative control among multiple agents. Furthermore, some potential uncertainties in practical applications are taken into consideration. Specifically, we investigate prescribed performance control (PPC) for multiple parametric strict feedback (PSF) systems subject to time-varying uncertainties, such as polarity reversal and parameter variations, which are common in battery packs. The main contributions of this paper are threefold: (1) It addresses a more challenging case in which the uncertainties in the agents’ models are time-varying, including unknown control coefficients and uncertain parameters. (2) Both the steady-state control objective and the transient performance are guaranteed simultaneously. (3) The analysis is simplified by designing a one-dimensional parameter estimator and eliminating the constraint on initial conditions through a simple and reasonable setting. Simulation studies are conducted to demonstrate the effectiveness of the proposed control scheme, and a comparison with traditional methods is presented. This work provides a theoretical basis for the design of BMS.

World Electric Vehicle Journal doi: 10.3390/wevj17060282

Authors: Jasem Alazemi Jasem Alrajhi Khalid Abdullah Alkhulaifi Nawaf Ali Alhaifi

This study presents a comprehensive techno-economic and environmental evaluation of ten standalone solar-powered mobile charging station configurations for mini electric vehicles (MEVs) in Kuwait, simulated using HOMER Pro (v3.18.4). The configurations span DC–AC and pure DC-bus architectures, fixed and tracking photovoltaic (PV) systems, hybrid designs incorporating diesel generator backup, and fully renewable zero-emission systems. All configurations were evaluated under identical load demand (6460 kWh/year), solar resource, and economic assumptions derived from Kuwait’s desert climate at Al-Wafra farms (28°33′52.7″ N, 48°03′45.8″ E, annual average GHI = 5.49 kWh·m−2·day−1). Performance was assessed using Net Present Cost (NPC), Levelised Cost of Energy (LCOE), annual PV energy production, CO2 emissions, Energy Production Density (EPD), Renewable Fraction (RF), and the PV Energy Production-to-Load Ratio (PV-EPTLR). The results demonstrate that two-axis tracking on a DC-bank architecture without a generator (System 8) achieves the highest annual PV output of 13,635 kWh/year, representing a 36% increase over a fixed-tilt DC-bank system while eliminating 100% of operational CO2 emissions. Among the hybrid configurations, vertical single-axis tracking on a DC-bank architecture with generator backup (System 6) yields the lowest lifecycle cost (NPC = USD 6271.8; LCOE = 0.0751 USD/kWh), representing a 57% reduction relative to the fixed-tilt DC–AC baseline. EPD analysis confirms that tracking-based systems improve structural energy efficiency by up to 36%, making them particularly suitable for mobile and weight-constrained deployments. The findings provide actionable guidance for deploying sustainable off-grid MEV charging infrastructure in regions with limited grid access, offering a scalable pathway toward zero-emission rural transportation in solar-rich arid environments. The study further provides a systematic comparison between DC–AC and pure DC-bank charging architectures under identical operating conditions.

World Electric Vehicle Journal doi: 10.3390/wevj17060281

Authors: Chunsheng Wang Xiaoshuang Hou Xinyao Zhou Bingbing Luo

With the in-depth advancement of the “dual carbon” strategy, Proton Exchange Membrane Fuel Cells (PEMFCs), as efficient and clean energy conversion devices, show great potential in the fields of transportation power and stationary power generation. For multi-stack fuel cell systems, a hierarchical optimization strategy based on Pareto decoupling and real-time correction is presented to achieve system efficiency improvement and balanced management of stack aging. Firstly, the Forgetting Factor Recursive Least Square (FFRLS) method is adopted to online identify the parameters of the system’s net output power-efficiency curve. Furthermore, in the steady-state layer, the Arithmetic Optimization Algorithm (AOA) is used to construct an efficiency-optimal candidate solution set. The Dijkstra algorithm is combined to search for the optimal power gradient path, generating a reference power table. In the dynamic layer, with the reference power table as the basis, the AOA algorithm is used to take efficiency optimization as the goal. Load fluctuations are suppressed in real time through strong constraints, realizing the balance between dynamic efficiency and operational stability. This method ensures the stable operation of the system and significantly improves the overall economy and adaptability of power allocation. Simulation results show that this strategy can effectively improve the overall operating efficiency of the system, slow down the stack aging rate, and ensure the stable operation of the system.

World Electric Vehicle Journal doi: 10.3390/wevj17060280

Authors: Salinee Santiteerakul Sakgasem Ramingwong Apichat Sopadang Korrakot Yaibuathet Tippayawong Poti Chaopaisarn Jirapat Wanitwattanakosol Boontarika Paphawasit Suttinee Sawadsitang Tisinee Surapunt

Despite growing scholarly attention to electric vehicle adoption in Southeast Asia, no study has systematically compared battery electric vehicle (BEV) readiness across Thailand, Lao PDR, and Vietnam using a unified demand–supply framework. This paper develops and applies a seven-dimension BEV Country Readiness Assessment framework encompassing supply-side factors (parts and materials sourcing, manufacturing, and after-sales support), demand, and enabling environment (legal and regulatory, government support, and market development), with maturity scores (1–5) assigned across 16 sub-dimensions based on national statistics, industry reports, and primary field research conducted between August 2024 and March 2026. Thailand scores highest overall (3.43/5.0) as a regional production hub with balanced readiness; Vietnam follows (3.26/5.0) with the highest demand score driven by VinFast’s ecosystem; and Lao PDR scores lowest (1.95/5.0) yet exhibits a notably high EV/ICE registration ratio driven by fleet-led adoption in a very small absolute market. The findings reveal complementary rather than competitive roles within the regional BEV value chain, with energy security, supply chain dependency on China, and institutional capacity identified as critical determinants of long-term readiness.

World Electric Vehicle Journal doi: 10.3390/wevj17060279

Authors: Jose del C. Julio-Rodríguez Pedro S. Gonzalez-Rodriguez Stefania Matilde Amaya-Sandoval David Sebastian Puma-Benavides Milton Israel Quinga-Morales Javier Milton Solís-Santamaria Edilberto Antonio Llanes-Cedeño

This study presents a novel methodology for optimizing energy management strategies in heavy-duty Fuel Cell Hybrid Electric Vehicles (FCHEVs) with pantograph charging systems. The approach integrates machine learning (ML) techniques to predict energy demand, optimize the power distribution between the battery and fuel cell, and enhance overall efficiency. The methodology involves clustering vehicle and road data, supervised ML classification, and zonification of routes for adaptive energy management. The proposed system was validated using real-world driving data from five different routes in Germany. The results indicate a significant improvement in hydrogen consumption and fuel cell degradation compared to conventional control strategies. This research establishes a framework for advanced energy management in heavy-duty hydrogen-powered electric vehicles.

World Electric Vehicle Journal doi: 10.3390/wevj17060278

Authors: Qiuchen Yun Zihan Xu Yefan Song Yuqi Liu Fang Zhang Peijun Li

Given the complex operating conditions and latent faults exhibited by electric vehicle charging infrastructure amid massive order volumes, traditional monitoring methods based on thresholds or single statistical metrics struggle to detect dynamic, time-varying anomalies. This paper proposes a method for identifying and tracing the operational status of charging facilities based on the k-shape time-series clustering algorithm. This method directly uses charging current time series as the research object, eliminating the cumbersome manual feature extraction process. By utilizing a shape-based distance (SBD) metric strategy, it overcomes common time-series data issues such as phase shifts and amplitude scaling while preserving the integrity of the time dimension. Through iterative calculation of cluster centroids, the algorithm successfully and adaptively classifies massive amounts of data into typical clusters such as “standard charging,” “deep oscillation,” and “power-limited.” Based on the clustering results, this paper further constructs a “shape-operating condition” mapping mechanism. Combined with a Bayesian posterior probability model, this enables the localization of high-risk “vehicle-charger” combinations statistically associated with abnormal waveforms. Empirical studies demonstrate that this method can effectively identify equipment performance degradation at the micro-level of waveforms and provide prioritized inspection clues for the intelligent operation and maintenance of charging networks.

World Electric Vehicle Journal doi: 10.3390/wevj17060277

Authors: Savvas Nikolaidis Paraskevas Koukaras

The rapid development of autonomous vehicles is based mainly on their ability to accurately perceive their environment, where artificial intelligence and computer vision act as the core of environmental perception. In this regard, deep learning-based perception architectures have revolutionized the field of autonomous driving. However, as the use of single sensors fails to ensure reliability in complex scenarios, multimodal sensor fusion has become an essential part of modern deep learning architectures. In this context, covering the literature from 2020 to 2025, we analyze the transition from traditional Convolutional Neural Networks (CNNs) to modern Vision Transformers (ViTs) and explore data fusion design methodologies at various processing levels. In addition, significant limitations related to adverse weather conditions and dynamic environments, computational resources and overall quality and management of data are identified. The conducted comparative analysis indicates that vision-transformer and multimodal fusion methodologies provide higher accuracy in perception tasks but at the cost of increased computational requirements and sensor synchronization challenges. Finally, it becomes clear that achieving full autonomy requires further research in subjects such as collaborative perception, unsupervised domain adaptation and the creation of lightweight models, thus offering a roadmap for future developments.

World Electric Vehicle Journal doi: 10.3390/wevj17060276

Authors: Ching-Chuen Chan Tianlu Ma Xiaosheng Wang Yibo Wang Hanqing Cao Chaoqiang Jiang

Driven by global energy transformation and the progress of artificial intelligence technology, traditional automotive engineering is undergoing profound changes. Transportation is rapidly advancing toward electrification and intelligence. Against this background, this paper identifies three emerging paradigms for the development of electric vehicles: Heart Revolution, Brain Evolution, and Network Integration. This paper points out that automobiles are evolving from traditional one-way energy consumers to dynamic energy nodes in smart grids. With the support of artificial intelligence technology, the role of automobiles is also shifting from a simple means of transportation to an intelligent mobile terminal. At the same time, this paper focuses on analyzing the application of the integration theory of “Four Networks and Four Flows” in automobile upgrading. The theory does not focus on the optimization of a single node unit but emphasizes a systematic perspective to improve overall performance and support sustainable development. This paper suggests that the development of the automobile industry must be deeply integrated with the humanity world, information world and physical world. By building a five-in-one architecture of “Human–Vehicle–Road–Cloud–Satellite”, the automobile industry could follow a practical pathway toward coordinated development. At the same time, breakthroughs in core technologies such as solid-state batteries and wide-bandgap semiconductors are also imminent. This paper aims to provide a sustainable and high-performance automobile development path and integrate the concept of human-oriented design into it. Meanwhile, China’s new energy vehicle industry is used as a representative context to illustrate its engineering and industrial implementation.

World Electric Vehicle Journal doi: 10.3390/wevj17050274

Authors: Jurica Perko Danijel Topić

The rapid growth of electric vehicle (EV) adoption requires the scalable and cost-effective deployment of publicly accessible charging infrastructure, where cost-effectiveness is understood in terms of infrastructure reuse rather than explicit economic optimisation. Integrating slow AC charging units into existing public lighting networks represents a promising infrastructure reuse strategy, though spatial feasibility, electrical constraints, and regulatory requirements must be addressed. This study proposes an integrated GIS–MCDA–MILP framework for the optimal allocation of EV charging stations within public lighting systems. GIS-based spatial analysis identifies feasible poles based on parking accessibility and demand indicators, while MCDA ranks candidate locations and a MILP model determines optimal deployment under capacity constraints and phased rollout scenarios. The framework also incorporates AFIR-based policy benchmarking to assess compliance under current and future EV adoption levels. A real-world case study identifies 1223 feasible poles with a structural hosting capacity of 368 chargers. The results demonstrate that such integration is viable at the spatial and cabinet-capacity planning level but structurally limited, with a critical fleet growth multiplier of approximately 3.4 identified as the threshold beyond which lighting-integrated deployment alone becomes insufficient for AFIR compliance. The proposed framework advances the state of practice by coupling spatial, electrical, and regulatory analysis within a single reproducible methodology, offering a transferable decision-support tool for sustainable urban EV charging planning.

World Electric Vehicle Journal doi: 10.3390/wevj17050275

Authors: Lingqing Guo Kangliang Zheng Xiucheng Wu Jinhong Wang Xiaofeng Lai Peiyuan Deng Lv He Yuan Cao Chengying Zeng Xiaoyu Dai

Accurate estimation of the internal spatial-temporal temperature distribution is crucial for the safety and performance management of lithium-ion batteries. However, traditional lumped parameter models overlook spatial gradients, while numerical methods for partial differential equations (PDEs) incur high computational costs. This paper proposes a hard constraint physics-informed neural network (HCPINN) framework for the real-time reconstruction of the axial temperature field in 18,650 cylindrical batteries. By restructuring the neural network’s solution space through distance functions, the Robin boundary conditions are strictly embedded as hard constraints, ensuring exact satisfaction of the prescribed Robin boundary conditions within the mathematical model and eliminating boundary loss terms. An electro-thermal coupled model considering the Arrhenius effect and state-of-charge (SOC) dependent internal resistance is integrated into the loss function to capture the nonlinear heat generation dynamics. Experimental validation across discharge rates from 1C to 4C demonstrates that the HCPINN achieves high estimation accuracy with a mean absolute error (MAE) below 0.34 °C. Furthermore, by leveraging the continuous differentiability of the model, this study quantifies the evolution of spatial temperature gradients and reveals the ideal heat transfer coefficients required for thermal equilibrium are inverted, providing a quantitative basis for the design of advanced battery thermal management systems (BTMS).

World Electric Vehicle Journal doi: 10.3390/wevj17050273

Authors: Benjamin Reuter

There is an important debate about the appropriate policy measures for reducing greenhouse gas (GHG) emissions in the transport sector. Strong expansion of battery electric vehicles (BEVs) following a ban on the registration of new vehicles with internal combustion engines (ICEs) by 2035 is a prominent but controversial proposal. To evaluate achievable GHG emission reductions, it is essential to understand the temporal dynamics of such a fleet transition. This study provides a time-resolved, policy-oriented quantification of annual and cumulative lifecycle GHG emissions during this process. Therefore, it uses an annual simulation model to assess GHG emissions from vehicle production and use during the transition of Germany’s passenger car fleet between 2019 and 2060. The analysis compares an ICE registration ban by 2035 with alternative scenarios and evaluates the effects of electricity decarbonization, greener BEV production, and the supply of additional Zero Emission Fuels (ZEFs). This study reveals a substantial time lag of 10–20 years between changes in new vehicle registrations and effective emission reductions. Even with a complete ICE ban by 2035, annual GHG emissions decline by only 3.7% by 2030 relative to 2025, while cumulative emissions over this period fall by just 1.6%. Larger reductions occur later, reaching 39% in 2040, 77% in 2050, and 82% in 2060 compared with 2025; cumulative emissions until 2060 decrease by 45%. Without an ICE ban and with a 75% BEV share from 2035 onward, cumulative reductions fall to 34%. Introducing additional ZEFs equivalent to 10% of 2030 fuel demand increases this value to 41%, compensating for much of the lower BEV uptake.

World Electric Vehicle Journal doi: 10.3390/wevj17050272

Authors: Chunyan Gao Yimeng Han Kunfeng Liang Min Li Shiman Su Yun Zhu

Traditional optimization methods for interior permanent magnet synchronous motors (IPMSMs) often treat the stator and rotor as independent design domains, which limits the potential for suppressing torque fluctuations due to the neglected electromagnetic coupling between these components. This paper proposes a synergistic optimization strategy for a 120 kW IPMSM, aiming to overcome the inherent limitations of conventional unilateral optimization in design space exploration and achieve global performance enhancement through cross-domain collaboration. By establishing a unified surrogate model incorporating both stator slot geometries and rotor pole topologies, the collaborative effect of seven high-sensitivity design variables is systematically analyzed. The NSGA-II algorithm, coupled with a Kriging surrogate model, is employed to navigate the complex trade-offs among average torque, torque ripple, and cogging torque. Results demonstrate that the synergistic approach achieves a 28.1% reduction in torque ripple while maintaining high average torque, demonstrating superior improvement over conventional stator-only or rotor-only optimization schemes. Analysis based on Maxwell stress tensors and air-gap permeance functions reveals that the proposed method achieves simultaneous suppression of cogging torque and torque ripple by effectively harmonizing the 24th and 48th spatial harmonics. This study provides an efficient synergistic design methodology for the comprehensive performance enhancement of traction motors, offering practical reference value for the engineering development of high-performance electric vehicles.

World Electric Vehicle Journal doi: 10.3390/wevj17050271

Authors: Angella Puspita Isti Surjandari Romadhani Ardi

Program KBLBB was implemented to reduce carbon emissions and mitigate climate change by 2030. Total sales of Battery Electric Vehicles (BEVs) in Indonesia until June 2025 are 107,428, with the increase in sales resulting in a proportional rise in EV battery waste. EV battery waste requires comprehensive policy recommendations for its management, as in Indonesia. The goal of this research is to develop a sustainable assessment for an EV battery waste management model that addresses environmental, economic, and social perspectives. The assessment is carried out using the End-of-Waste framework model, Reuse, with recycling technology hydrometallurgy for Nickel Manganese Cobalt (NMC) and Lithium Ferro Phosphate (LFP) batteries. The results show that the environmental impacts of waste from NMC batteries are 20% smaller than those of LFP batteries, with 80% of the impacts. The total cost of waste from LFP batteries is lower than that of NMC batteries. The S-LCA risk score shows the same results for waste from NMC and LPF batteries: a very high risk for actual female employment, unequal remuneration, no collective bargaining indicators, and no right to organize. Sensitivity analysis results for EV battery waste management model for NMC batteries with hydrometallurgy, collection level of 30%, and recovery rate of 85%.

World Electric Vehicle Journal doi: 10.3390/wevj17050270

Authors: Jianxin Zhang Mingyang Yin Xinyue Li Fubo Li Xinyi Zhang Li Li

Modern electric vehicle (EV) charging stations must increase their adaptability to dynamic demand patterns driven by users’ heterogeneous charging behaviors, which often result in high spatial–temporal fluctuations. This study develops an agent-based model to accurately evaluate the potential of partial-charging strategy in addressing this issue, taking into account the influence of drivers’ heterogeneous waiting patience. The simulating results indicate that the operational efficiency of the charging station and the level of crowding are most sensitive to changes in vehicle arrival rates and the total number of charging stations. However, individual-level heterogeneity in waiting patience emerges as the core factor preventing limitless queuing increase. Compared with other strategies, the partial-charging strategy improves the turnover of charging stations by reducing per-vehicle charging duration, allowing stations to adapt to varying charging demand conditions without capacity expansions. Setting the charging threshold at 80% state of charge allows the stations to efficiently serve twice the demand level as under full-charging strategy, while a 70% threshold may increase this adaptability by approximately 2.5 times. This study provides structured recommendations for the strategic and adaptive deployment of the partial-charging strategy in alleviating queue-related inefficiencies of charging stations.

World Electric Vehicle Journal doi: 10.3390/wevj17050269

Authors: Athulya Jyothi V Lakshman Rao S. Paragond Bindu S

This study details the modelling and simulation analyses performed on a mathematically modelled permanent magnet-assisted synchronous reluctance motor (PMa-SynRM) driven by a field-oriented controlled (FOC) voltage source inverter (VSI) coupled with a half-bridge bidirectional buck-boost DC/DC converter for two-wheeler electric vehicle (EV) applications. The 5 kW, 1500 rpm PMa-SynRM employed here has a shorter response time and is also naturally lighter and cost-effective, making it suitable for two-wheeler EVs. Field-oriented control simplifies the control strategy for PMa-SynRM by decoupling torque and flux, effectively matching the behaviour of a DC motor. A half-bridge buck-boost converter is a DC-DC converter capable of bidirectional power flow, stepping up and down voltages. This makes it ideal for both motoring and regenerative braking in electric vehicles. The buck-boost converter with its controller effectively adjusts the inverter and battery voltage for efficient power flow during motoring and maximum power recovery during regenerating braking. The developed model aims at demonstrating forward and reverse motoring, as well as forward and reverse braking to validate the four-quadrant torque-speed characteristics of two-wheeler EVs. The proposed model attains less than 2% torque ripple and less than 1% speed ripple, respectively. Further, the current ripples are minimised to reduce losses and to improve efficiency. The work presented in this paper implements a PMa-SynRM-based drive system for EVs, a technology which is in the exploratory stage and not commercially widespread. This adds novelty to the proposed work. A MATLAB Simulink environment was used for modelling and simulation.

World Electric Vehicle Journal doi: 10.3390/wevj17050268

Authors: Yongxing Wang Chaoru Lu Dongfan Xie

The transportation sector is the main source of environmental pollution in cities, fueling the energy crisis [...]

World Electric Vehicle Journal doi: 10.3390/wevj17050267

Authors: Xuyichen Yan Lan Wu Xinfei Zhang Ming Yang Lintong Han Qian Chen

The Multi-Destination Vehicle Routing Problem (MD-VRP) with a heterogeneous electric fleet is a critical challenge in optimizing commuter services for large-scale institutions and logistics operations. To address the complexities of electric fleet composition uncertainty and multi-center routing in “micro-city” campus environments, this paper establishes a robust multi-objective programming model. The model aims to simultaneously minimize three conflicting objectives, the total number of vehicles, total driving distance, and total electric energy consumption (kWh), under constraints of flow conservation and vehicle availability. Considering the nondeterministic polynomial-time hard (NP-hard) nature of the problem, a novel two-stage hybrid heuristic algorithm is proposed. In the first stage, a Modified Kruskal’s algorithm is employed to aggregate scattered stops into optimized clusters to reduce dimensionality. In the second stage, a State-Compressed Dynamic Programming (SC-DP) algorithm is applied to determine the optimal routing and electric vehicle type selection for each cluster. The methodology is validated using a case study of a large-scale campus network with 100 nodes. The optimization results identify an optimal fleet configuration of 41 campus electric commuter vehicles across three specific types (capacities of 45, 55, and 60), resulting in an annual total energy consumption of 5893.98 kWh. Compared with a global Ant Colony Optimization (ACO) baseline in this case study, the proposed framework reduces the required fleet size by 22.6% and annual energy consumption by 9.2%; however, this comparison should be interpreted as a preliminary case-study benchmark because the proposed method adopts a decomposition-based “Cluster-First, Route-Second” strategy. The results indicate that the approach achieves higher solution efficiency, offering an economically and environmentally friendly scheme for electric vehicle fleet operations.

World Electric Vehicle Journal doi: 10.3390/wevj17050266

Authors: Zhengzhi Ma Zhenfei Zhan Tao Liu Decong Kong Lei Zhu

Electric vehicles introduce new considerations for pedestrian safety because their lower operating noise at low speeds may reduce pedestrian detectability in urban traffic environments. This study proposes a simulation-based integrated active–passive pedestrian protection framework for electric vehicles by linking automatic emergency braking, active hood deployment, and post-crash head injury assessment. A total of 688 valid pedestrian–vehicle crash records from the National Highway Traffic Safety Administration database were analyzed, and 5 representative pedestrian crash scenarios were constructed through clustering-informed scenario screening and a benchmark pedestrian AEB scenario. The scenarios were reconstructed in a PreScan–Simulink co-simulation environment to evaluate a time-to-collision-based AEB strategy, while the active hood system was assessed using multi-body dynamics simulation and finite element head impact analysis. The AEB results showed that three scenarios were avoided before pedestrian contact, whereas two remained unavoidable, with residual impact speeds of approximately 31.5 km/h and 46 km/h. The hood reached a stable deployed posture within approximately 0.1 s under the modeled conditions. The HIC15 results at eight selected impact points showed that speed reduction and hood deployment generally reduced head injury metrics, but full compliance with the reference HIC15 threshold of 1000 was not achieved at all points. These findings suggest that the proposed strategy can improve simulated pedestrian head protection performance under selected electric vehicle crash scenarios, while further structural optimization, experimental validation, and cost–benefit assessments are still required.

World Electric Vehicle Journal doi: 10.3390/wevj17050265

Authors: Ayrton Lucas Lisboa do Nascimento Bruno Santana de Albuquerque Josivan Rodrigues dos Reis Rafael Maximino Bastos Carminda Célia Moura de Moura Carvalho Ubiratan Holanda Bezerra Jonathan Muñoz Tabora Maria Emília de Lima Tostes

This paper presents an integrated validation framework for EV taxi charging-hub plans that combines spatial accessibility, grid deployability, and operational performance. Candidate hub configurations are first generated through a demand-weighted p-median model based on 175 taxi stands and 2825 cooperative members in Belém, Brazil. The assigned demand is then translated into charger requirements through stochastic sizing, and the resulting infrastructure is screened against the available headroom of 12,905 medium-voltage transformers. Finally, the selected solution is evaluated through an Erlang-C queueing model under peak-demand concentration. The final plan, obtained with 14 hubs, achieved a weighted mean distance of 0.724 km and a weighted P95 distance of 1.488 km, while requiring 46 chargers and 2610 kW of installed capacity. Of these, 45 chargers were successfully allocated in the grid-screening stage, corresponding to a placement rate of 97.83%.

World Electric Vehicle Journal doi: 10.3390/wevj17050264

Authors: Kritzman P. Jooste Ali Almaktoof Mohamed T. Kahn

This study presents a simulation-based framework for battery-degradation-aware routing in electric vehicles by integrating physics-informed battery state estimation with decision-level navigation logic. A hybrid estimation approach combining spatially distributed fiber-optic sensing with complementary Kalman filtering strategies is used to reconstruct core temperature, surface temperature, state-of-charge, and mechanical degradation indicators in real time. These estimated states are supplied directly to an intelligent routing module, enabling charging station selection that is both physically reachable and aware of thermal- and health-related constraints. The results demonstrate that routing decisions informed by battery state estimation consistently avoid high-risk thermal and swelling conditions while maintaining range feasibility. By explicitly incorporating mechanical degradation indicators into the routing logic, the framework addresses a key gap in prior studies where battery swelling and navigation were treated independently. Overall, the findings confirm that estimator-driven, degradation-aware routing can improve operational safety, reduce range anxiety, and support more reliable electric vehicle navigation. The study establishes a simulation-first foundation for future experimental validation, adaptive policy refinement, and broader deployment of battery-degradation-aware decision-making in electric mobility systems.

World Electric Vehicle Journal doi: 10.3390/wevj17050263

Authors: Zibo Ye Kai Wen Xingfeng Fu Feng Pei

With the rapid increase in electric vehicle (EV) ownership, the strategic planning and layout of charging infrastructure have become essential to encourage EV adoption. This study introduces a comprehensive multi-objective optimization method for selecting locations and designing layouts for high-power and extreme fast charging stations. By thoroughly accounting for user charging demands, economic expenses, and traffic conditions, a multi-objective optimization mathematical model is created aiming to minimize user time and costs while maximizing service capacity and user satisfaction. The model combines queuing theory, network topology analysis, and genetic algorithms to simultaneously handle discrete variables related to station placement, continuous variables for charging pile setup, and complex constraints. Using Panyu District in Guangzhou as a case study, a simulation model with 20,000 electric vehicles and 20 high-power and extreme fast charging stations is developed, focusing on the optimal arrangement of 120 kW, 240 kW, and 480 kW charging piles. The simulation results demonstrate that the optimized charging station layout scheme (13 units of 120 kW, 6 units of 240 kW, and 1 unit of 480 kW) lowers overall costs by 6.74%, reduces user charging waiting time from 1.54 h to 0.65 h, improves user satisfaction by 8.1%, and cuts the peak-to-valley difference in charging load from 900 kW to 450 kW. This work offers both theoretical insights and practical recommendations for the effective planning of electric vehicle charging infrastructure.

World Electric Vehicle Journal doi: 10.3390/wevj17050262

Authors: Shen Li Qingshan Xu

The output voltage harmonics of electric vehicle (EV) charging converters directly affect grid power quality. This paper proposes a harmonic analysis method based on geometric algebra (GA), which employs a multivector representation of signals and least squares estimation to accurately extract fundamental, integer-order, and inter-harmonics. A coupling coefficient is defined to quantify the phase correlation between frequency components. Based on measured data, harmonic characteristics under four typical operating conditions are analyzed, and an adaptive PID controller is designed to dynamically adjust the virtual resistance for harmonic suppression. The results show that the GA method significantly reduces spectral leakage under non-integer-period sampling conditions, with amplitude estimation errors below ±2%. The total harmonic distortion (THD) decreases with increasing active power and increases with reactive power injection. The droop coefficient exhibits a non-monotonic effect, while reducing the proportional gain raises the THD. Adaptive control reduces the average THD by 14.0–28.5% with a total response time of less than 0.05 s. The coupling coefficient C13 is strongly positively correlated with THD and negatively correlated with the maximum Lyapunov exponent computed using the Rosenstein small-data method (correlation coefficient −0.85), confirming the intrinsic relationship between coupling and stability. Compared with fast Fourier transform (FFT) and other methods, GA achieves higher accuracy under short data records and non-integer-period sampling. This paper provides a complete theoretical framework and engineering solution for harmonic suppression in charging converters.

World Electric Vehicle Journal doi: 10.3390/wevj17050261

Authors: Krisztian Horvath

Considerable progress has been made in predicting nominal NVH behavior in electric drivetrains, but the acoustic scatter observed across manufactured units remains insufficiently understood. In practice, nominally identical drive units may still exhibit noticeably different tonal behavior because small deviations in gears, shafts, bearings, fits, centering features, or assembly phase modify the excitation, transfer, and radiation mechanisms of the system. This review examines how manufacturing and assembly variability influences NVH performance in electric drive units and e-axles, with particular focus on the rotor–shaft–gear–bearing–housing system. Unlike broader EV NVH reviews, the present work focuses specifically on variability-induced acoustic scatter and its propagation along the drivetrain NVH generation and transmission path. To support transparency and consistency, the literature search and selection process followed a structured, PRISMA-inspired approach across Scopus, Web of Science, Google Scholar, and SAE Mobilus for the 2015–2026 period. From 387 identified records, 50 studies were retained after duplicate removal, screening, and full-text assessment. The selected literature was synthesized into eight thematic categories: imbalance; run-out and eccentricity; bearing clearance and preload; spline and pilot centering; thermal effects; phase indexing; transmission error and sidebands; and end-of-line NVH diagnostics. The reviewed literature shows that manufacturing- and assembly-induced deviations can significantly alter transmission error, sideband structure, shaft-order content, and final tonal response, even when individual components remain within nominal tolerance limits. Beyond synthesizing the evidence base, the review organizes existing modeling and diagnostic practices into a structured framework for variability-aware NVH assessment, based on explicit deviation parameterization, hierarchical model fidelity, intermediate excitation metrics, thermal-state awareness, and closer integration with production and measurement data. Overall, the findings support a shift from nominal NVH assessment toward robustness-oriented, production-representative interpretation and future prediction of acoustic scatter in electric drivetrains.

World Electric Vehicle Journal doi: 10.3390/wevj17050260

Authors: Piersilvio Marcolin Augusto Bozza Andrea Cazzaniga Filippo Colzi

This paper presents a comprehensive methodology for evaluating the flexibility potential of Electric Vehicle (EV) charging infrastructures from the perspective of a Charge Point Operator (CPO). The proposed framework is general and applicable to different types of charging infrastructures, provided that a set of operational assumptions is satisfied. These include unidirectional smart charging (V1G), AC charging sessions, preservation of user energy delivery when providing flexibility, and explicit modeling of rebound effects induced by temporal load shifting, requiring subsequent recovery of the shifted energy. The methodology is then applied to a real-world workplace charging facility to quantify the amount and temporal distribution of flexibility under different baseline charging strategies and levels of on-site photovoltaic integration. The analysis shows that a significant share of daily energy demand (i.e., between 20% and 36%) can be made available for flexibility services within the considered assumptions. Furthermore, the results highlight a strong operating cost trade-off between local optimization strategies and participation in system-level flexibility markets in the considered case study.

World Electric Vehicle Journal doi: 10.3390/wevj17050259

Authors: Dianfeng Zhang Tianya Xu Juntao Shi Xuefeng Hou Yanlai Li

With the rapid advancement of technology, robotaxi services have emerged as a pivotal development direction within the transportation industry. Currently, this field is at a critical juncture transitioning from technological R&D to commercial operations, with service coverage continuously expanding and a pressing need to enhance and optimize service quality. This study aims to refine the service quality of robotaxis, elevate user-perceived experiences, and boost satisfaction levels. Through a literature review, we systematically examined the development status and service pain points of robotaxi services both in China and abroad. Leveraging grounded theory, we identified 21 service elements, and designed a bidirectional questionnaire for empirical investigation. Methodological robustness was confirmed through multi-source cross-validation. Then, we classified these elements using the Type-II Candy Model, and prioritized them based on the Average Satisfaction-Dissatisfaction metric. The findings reveal that the service elements of robotaxi services encompass 8 Differentiate factors, 1 Must-be factor, 4 One-dimensional factors, 5 Attractive factors, and 3 Indifferent factors. This study systematically dissects user requirements, enabling enterprises to improve service quality and standards more effectively by aligning with the requirement categories and their priority rankings. It facilitates the construction of a systematic service delivery and evaluation framework, ultimately better meeting consumer requirements.

World Electric Vehicle Journal doi: 10.3390/wevj17050258

Authors: Gang Liu Jiashuai Fang Jian Liu Jiashuai Xue Jiaxu Zhao

Achieving simultaneous trajectory accuracy and dynamic stability is challenging for four-wheel independent drive (4WID) electric vehicles under near-limit conditions. To effectively resolve this internal control conflict, this paper proposes a novel normalized stability index that accurately quantifies real-time instability risks. Based on this index, a hierarchical adaptive coordinated control architecture is developed, utilizing sliding-mode control for active front-wheel steering to follow trajectories and a fuzzy-logic yaw moment controller to maintain stability. To prevent over-control in safe driving regions, an adaptive weighting mechanism seamlessly adjusts the stability interventions according to the proposed index. Hardware-in-the-loop (HIL) experiments demonstrate that the proposed method lowers sideslip risks on low-adhesion tracks. During a variable-curvature slalom, it reduces the lateral RMSE by 15.08% and decreases the maximum additional yaw moment from 118 N·m to 32 N·m, thereby mitigating excessive control effort, minimizing steering conflicts, and structurally improving the actuation efficiency of the 4WID system.

World Electric Vehicle Journal doi: 10.3390/wevj17050257

Authors: Renhui Luo Rong Yang Hang Yang Wei Huang

Accurate online tracking of state of charge (SOC) inconsistency in lithium-ion battery packs is essential for safety. It is equally critical for effective battery management in real-world operation. To achieve robust performance in dynamic battery environments characterized by temperature fluctuations and cell aging, a method combining enhanced Rime optimized-unscented particle filter (ERIME-UPF) with cubature smooth variable structure filter-varying boundary layer (CSVSF-VBL) is proposed. The cell mean-difference model is used to simulate the behavior characteristics of the battery module, including the hysteresis effect dynamic migration model, and the Rint model. First, module SOC is estimated using an ERIME-UPF, which adaptively adjusts the noise covariances of UPF via the enhanced RIME optimizer. Simultaneously, CSVSF-VBL employs the Rint model to estimate cell SOC inconsistencies, incorporating capacity and internal resistance coefficients into the second-order performance chattering to better capture cell inconsistency. Experiments focus on LiFePO4 batteries under various inconsistencies, temperature, and aging states. The results show that ERIME-UPF achieves an average mean absolute error (MAE) of 0.33% for module SOC estimation, while CSVSF-VBL achieves a peak MAE of 3.28% for cell SOC estimation. Demonstrating superior accuracy and robustness in tracking SOC inconsistency under dynamic and degraded operating conditions.

World Electric Vehicle Journal doi: 10.3390/wevj17050256

Authors: Sanjarbek Ruzimov Komiljon Tulaganov Shafkatbek Alimov Olimjon Tuychiev Akmal Mukhitdinov

Mild hybrid electric vehicles (MHEVs) are a cost-effective solution for reducing fuel consumption and emissions in the automotive sector, offering a low-level electrification alternative to battery electric and plug-in hybrid vehicles. This study uses the Equivalent Consumption Minimisation Strategy (ECMS) to investigate the optimal sizing of P2 MHEV powertrain components and the individual contributions of hybridisation features such as regenerative braking, idling fuel cut-off, load shifting and electric torque assist. Parametric simulations were performed by varying the power of the electric motor and the capacity of the battery for standard driving cycles. The results show that total fuel consumption for the NEDC driving cycle can be reduced by up to 29%, with regenerative braking providing the largest contribution. The optimal electric motor power for mild hybrid applications was found to be in the 20–30 kW range, depending on the driving cycle.

World Electric Vehicle Journal doi: 10.3390/wevj17050255

Authors: Yongbiao Yang Haichuan Zhang

During the process of vehicle–grid interaction, the charging load of electric vehicles shows significant uncertainty, which is driven by multiple user behavior variables: including the differentiated characteristics of users’ daily travel needs, as well as personalized charging habits, random charging periods, and dynamic changes in charging power demands. To address the scheduling challenges arising from the uncertainty of electric vehicle loads in the interaction between electric vehicles and the power grid, this paper proposes a multi-objective optimization scheduling method for the interaction between electric vehicles and the power grid, which takes into account the uncertainty of power sources and loads. This method can enhance the economic operation level of the power grid, increase the acceptance capacity of renewable energy, and improve the stability of the system. Firstly, this paper proposes an improved K-means clustering algorithm, which combines Monte Carlo sampling to achieve the generation and reduction of scenarios for electric vehicle load and photovoltaic output. Secondly, a scheduling framework based on the vehicle–grid collaborative interaction mode is constructed, and a random optimization scheduling model for photovoltaic storage electric vehicles is established. Finally, an example of a photovoltaic storage charging station in an industrial park is used for verification. The simulation results demonstrate the economic feasibility and effectiveness of this scheduling strategy.

World Electric Vehicle Journal doi: 10.3390/wevj17050254

Authors: Gian Luca Patrone Elena Paffumi

This work presents the results of an on-road test campaign on an aged mid-size battery electric vehicle. After a full charge, the vehicle was completely discharged by driving on the road, with different routes (combining speeds and road slopes) and payloads. The resulting driving range and discharged battery energy were measured. The results are compared with those obtained from previous laboratory test campaigns on a chassis dynamometer driving at constant speed or with the standardised testing protocols according to the WLTP. Considerations of the influence of environmental and route conditions on the usable battery energy during the on-road test are made. The new concept of virtual distance related to V2X applications is presented based on the UN GTR No. 22 dealing with in-vehicle battery durability. This is a new concept introduced to account for the additional ageing caused by battery cycling due to applications other than driving or charging.

World Electric Vehicle Journal doi: 10.3390/wevj17050253

Authors: Meiling He Xi Yang Xun Han Jin Zhang Xiaohui Wu Xiaolai Ma

Inspired by the practical need for reliable drone-assisted logistics under varying weather conditions, this study investigates the vehicle–drone collaborative routing problem with weather constraints and time windows. The objective is to minimize the total delivery cost, including vehicle fixed costs, vehicle travel costs, drone flight costs, and time window penalty costs. To capture the impact of weather conditions on drone operations, a wind-speed-dependent dynamic flight speed function is introduced. A mathematical model is formulated, and an adaptive large neighborhood search algorithm integrated with genetic operations is proposed to enhance both local search efficiency and global exploration capability. Computational experiments on benchmark instances demonstrate that the proposed algorithm obtains high-quality solutions across different problem scales. Compared with the adaptive large neighborhood search algorithm and the improved genetic algorithm, the proposed approach reduces the optimal total delivery cost by an average of 4% and 2%, respectively. Sensitivity analysis further shows that increasing wind speed levels and the proportion of no-fly periods reduces the number of drone service tasks and increases total system cost, highlighting the significant impact of weather conditions on vehicle–drone collaborative delivery systems.

World Electric Vehicle Journal doi: 10.3390/wevj17050252

Authors: Fan Xiao Hengxuan Li Kanjun Zhang

The proliferation of vehicle-to-grid (V2G) charging stations in distribution networks introduces both voltage regulation challenges and untapped reactive power resources. This paper proposes a reactive–active power coordination control strategy for grid-forming (GFM) V2G charging stations to achieve voltage regulation in radial distribution networks. First, a voltage–reactive power sensitivity matrix is analytically derived from the linearized DistFlow equations, quantifying the voltage influence of each V2G station. The sensitivity matrix is computed from the network topology and line parameters, and its accuracy under varying operating conditions is validated against nonlinear power flow solutions. Second, a dynamic residual reactive capacity model exploits the inverter apparent power margin without curtailing active power, and a sensitivity-weighted proportional allocation distributes the reactive power references among stations. Third, a two-timescale hierarchical control architecture is designed: the upper layer solves a quadratic programming problem every 60 s to determine optimal set-points, while the lower layer employs GFM droop control with a 1 ms response to track references and provide inertia support. Simulation results on a modified IEEE 33-bus system demonstrate that the proposed method reduces the maximum voltage deviation by 62% compared with active-power-only control, while maintaining a frequency nadir of 49.73 Hz, confirming negligible frequency performance degradation. Extended simulations covering a 24 h period with stochastic EV arrival and departure patterns as well as varying load conditions further confirm the robustness of the proposed strategy.

World Electric Vehicle Journal doi: 10.3390/wevj17050251

Authors: Xiansheng Ran Guang Yuan Shijie Ni

To address the complexities inherent in evaluating electric motorcycle ride comfort across diverse road profiles and operating speeds, this study establishes a systematic evaluation framework utilizing a multi-level fuzzy comprehensive assessment approach. Empirical investigations were conducted on asphalt, Belgian block, and speed-bump terrains at varying velocities. Triaxial acceleration data were acquired from the seat, footrest, and handlebar interfaces to compute weighted Root Mean Square (RMS) acceleration, Vibration Dose Value (VDV), and Power Spectral Density (PSD). By synthesizing subjective ratings, a correlation between tactile perception and objective metrics was derived to calibrate the two-level fuzzy model. Analysis reveals that vibration energy is predominantly concentrated in the vertical low-frequency domain (0–20 Hz) independent of test conditions. Notably, a 50% increase in velocity precipitated a 22.4% decrement in the comprehensive ride comfort index, degrading the classification from “Moderate” to “Fair.” The proposed framework provides a rigorous quantitative paradigm for vibration mitigation strategies and informed speed management in electric vehicle engineering.

World Electric Vehicle Journal doi: 10.3390/wevj17050250

Authors: Buasa Andy Mayingi Bonginkosi A. Thango Daniel Okojie

Electric vehicle (EV) adoption in South Africa remains constrained by high upfront purchase costs, limited charging infrastructure, and policy uncertainty, creating a need for lower-cost and locally relevant pathways to transport decarbonisation. This study evaluates the feasibility of converting a legacy light commercial pickup platform from internal combustion engine (ICE) propulsion to battery-electric propulsion through integrated component sizing, longitudinal vehicle simulation, and techno-economic assessment. A retrofit architecture comprising a traction battery, inverter-controller, electric motor, and DC-DC converter was developed using first-principles vehicle dynamics and energy-demand analysis. The resulting configuration employed a 40 kW AC induction motor, an approximately 28 kWh battery pack, a 40–60 kW inverter with 60 kW peak capability, and a 0.75–1.2 kW auxiliary DC-DC converter. Simulation over a representative 1000 s drive cycle showed stable speed tracking, sustained vehicle motion over approximately 10 km, and peak battery currents exceeding 300 A during acceleration, while regenerative braking reduced net cumulative energy consumption relative to gross demand. The economic analysis indicated that the retrofit pathway yielded the lowest cumulative total cost of ownership over most of a 10-year horizon, with breakeven relative to the used ICE baseline occurring at approximately 3.4 years. Lifecycle analysis further showed that the retrofit configuration achieved the lowest combined production and operational carbon burden among the compared vehicle pathways. These findings indicate that ICE-to-EV retrofitting of legacy light commercial vehicles can provide a technically feasible, economically competitive, and environmentally advantageous electrification strategy for South Africa and comparable emerging markets.

World Electric Vehicle Journal doi: 10.3390/wevj17050249

Authors: Jannatul Ferdouse Simone Ehrenberger Christian Wachter Mohamad Abdallah

CO2 emissions are rapidly rising with new records, and the transport sector is considerably contributing to GHG emissions. The critical transition towards electrification and sustainable development demands a radical change in the transport industry. One of many solutions is to analyze the environmental benefits of optimized vehicle production and recycling of the vehicle components after their usable life to reduce dependency on limited raw materials. The electric motor is one of the most crucial powertrain components, yet studies on the overall ecological profile of production and the end of its usable life are limited. This study examines the life cycle assessment (LCA) impacts of electric motors used in passenger cars and the potential recycling of their materials. The analysis covers the production and recycling of components, crucial elements, and permanent magnets. The results show that housing and rotor production have the highest impacts, mainly due to the presence of steel, aluminum and permanent magnets. The findings discuss e-motor recycling innovations, state-of-the-art methods and emission-reduction potentials of recycling. This paper also covers the understanding that a significant transformation to optimize the resource consumption in the manufacturing of crucial vehicle powertrain components and reduce waste after end-of-life could bring combined ecological advantages.

World Electric Vehicle Journal doi: 10.3390/wevj17050248

Authors: Jaijeet Singh Rathore Shreyas Hosakere Rajashekharachar Linus Hallberg

The performance attributes of a heterogeneous multi-battery pack system significantly impact the electric vehicle’s performance. This study aims to investigate the power reduction and energy utilization phenomena in heterogeneous battery pack configurations that arise due to an uneven current split, focusing on defining the power ability curves and usable energy for the mixed system. A Multiphysics-based system model has been developed to investigate the factors contributing to power loss and usable energy when the aged packs are mixed with fresh packs. Different methods, viz., scaled, aged, and interpolation, are proposed to estimate the power retention curves for one and two fresh packs mixing into the homogeneous system. Also, energy evaluation helps in identifying the impact on vehicle range, which is an important attribute of vehicle performance. Altogether, having power ability curves and usable battery energy (UBE) for a heterogeneous multi-pack system helps in defining the decision-making strategies for the refurbishment of ESS during replacement and maintenance activities in EVs. Some strategies are introduced at the end using aged and scaled methods to conduct the most conservative power estimations while pack mixing. Energy evaluation is performed at the ESS level, highlighting the impact of fresh pack on the aged system usable energy.

World Electric Vehicle Journal doi: 10.3390/wevj17050247

Authors: Delong Zhang Yubo Ma Hongan Wu

As a critical element of the drive motor, rolling bearings are susceptible to localized defects under complex loads and varying operating conditions. Such defects typically generate periodic transient shocks, which reflect bearing fault features. However, the accurate extraction of fault-related transient components becomes challenging due to strong noise influence. To address this issue, a concave sparsity-assisted generalized dispersive mode decomposition (CSA-GDMD) method is developed to enhance fault feature extraction. This method introduces a non-convex sparse model based on generalized mini-max concave (GMC) regularization to preprocess the vibration signal. The GMC penalty effectively suppresses background noise while better preserving the amplitude characteristics of the transient shocks. Subsequently, GDMD is applied to progressively extract transient shock components from the preprocessed signal and reconstruct the signal, resulting in more prominent fault-related transient components. The simulation results show that CSA-GDMD significantly improves the signal-to-noise ratio (SNR), from 6.5905 dB at −15 dB to 9.5122 dB at 5 dB, and reduces the root mean square error (RMSE) from 0.0280 to 0.0196. Consequently, the fault feature frequencies can be identified more clearly in the envelope spectrum, further confirming the accurate fault diagnosis capability of the proposed method for bearing faults under strong noise conditions.

World Electric Vehicle Journal doi: 10.3390/wevj17050246

Authors: Elena Malakhatka Mia Johansson Emanuella Wallin Albert Petersson David Steen

Vehicle-to-Grid (V2G) is increasingly recognized as a promising source of flexibility for low-carbon energy systems, yet its deployment remains limited in practice. While previous research has largely focused on technical feasibility and market integration, less attention has been paid to V2G as a multi-actor service system. This study addresses that gap by applying a service design perspective to the co-development of a V2G service blueprint in the Swedish context. The research was conducted through an exploratory multi-stakeholder co-design process. The resulting blueprint maps customer actions, frontstage and backstage processes, stakeholder interactions, and communication flows across the V2G service lifecycle. The study identifies several service-level challenges related to onboarding, coordination, pre-qualification, contractual complexity, and user-facing value communication. The findings show how service blueprinting can support the structuring, analysis, and early-stage design of V2G services, while also highlighting the need for further validation in pilot implementation and across different regulatory contexts.

World Electric Vehicle Journal doi: 10.3390/wevj17050245

Authors: Marco A. M. Ferreira Paulo G. Pereirinha João Pedro F. Trovão

This study assesses the impact of regenerative braking on lithium-ion battery aging and operational efficiency of lithium-ion batteries in urban electric buses using a Rainflow-based cycle-counting framework. A previously developed simulation platform based on Energetic Macroscopic Representation (EMR) is employed to reproduce realistic daily driving cycles. Battery degradation is quantified by combining the Rainflow Counting Method with Miner’s Rule, enabling cumulative damage assessment across different depth of discharge (DoD) levels and regenerative braking intensities, kbr. Four representative cycling profiles—fixed 50%, 60%, and 70% DoD and a variable mixed-use scenario—were simulated under regenerative braking intensities ranging from 0% to 100%. Results indicate that regenerative braking extends average battery lifespan by approximately 0.9 years while increasing daily driving range by around 6 km. Profiles with lower DoD values, particularly when combined with moderate regenerative braking (kbr ≈ 0.3), achieved the most favourable balance between cycle induced degradation and energy recovery. Although higher DoD scenarios deliver greater mileage gains, they also accelerate capacity fade. The variable cycling profile demonstrated robust and consistent performance, highlighting the benefits of route and load variability. Additionally, lifetime mileage analysis demonstrates that intermediate DoD levels combined with regenerative braking maximize cumulative energy throughput while preserving service life. Overall, the proposed methodology offers a computationally efficient and practically applicable approach for battery life assessment under dynamic operating conditions, offering valuable insights for optimizing energy management strategies and electric bus fleet operations.

World Electric Vehicle Journal doi: 10.3390/wevj17050244

Authors: Md Sazzad Hosen Amir Farbod Samadi Kashif Raza Maitane Berecibar

The second-life application of vehicle batteries is getting attention as millions of battery systems, modules, or cells are going to enter the market in the coming decade. The performance uncertainty with or without historical knowledge of the batteries’ vehicle usage is a concern. Moreover, detailed studies on second-life battery cell behavior is sparse and an improved understanding is required for reuse/repurpose. In this work, two second-life battery packs are dismantled, and the extracted prismatic and pouch Nickel–Manganese–Cobalt (NMC) cells with 141 Ah and 65 Ah, respectively, are extensively investigated to understand the second-life degradation behavior. The one-and-a-half-year-long test campaign has followed dedicated suitable stationary test matrices, generating a valuable dataset. The aging dataset is then filtered with the most correlated features via Pearson correlation analysis (PCA) and used to train different machine learning algorithms, resulting in a root-mean-square-error (RMSE) of 0.065 and 0.109 for prismatic and pouch cells, respectively, with the best-performing ElasticNet model validated against real-life stationary profiles. The developed framework is suitable for edge computation where the SoH could be evaluated online, facilitating state-based performance and lifetime extension.

World Electric Vehicle Journal doi: 10.3390/wevj17050243

Authors: Mahmoud Shaat Farhad Oroumchian Zina Abohaia May El Barachi

This study develops a multi-criteria and AI-assisted optimization framework that integrates the Analytic Hierarchy Process (AHP), K-means clustering, and Genetic Algorithm (GA) optimization within a Geographic Information System (GIS) environment to optimize electric vehicle (EV) charging station deployment across Abu Dhabi’s urban–rural gradient. The model generates a community-level Spatial Suitability Index (mean = 0.47) based on residential, commercial, and accessibility factors, which inform clustering into five deployment typologies reflecting distinct socio-spatial characteristics. GA-based spatial optimization under two policy pathways, Progressive and Thriving, balances accessibility, grid proximity, and utilization efficiency. Results show that the Thriving scenario achieves approximately 15–20% higher network coverage and equity compared to the Progressive case, demonstrating the value of adaptive, data-driven optimization for mixed urban–rural contexts. The integrated AHP–Clustering–GA approach provides a transferable and scalable blueprint for equitable, low-carbon mobility infrastructure planning in rapidly developing regions.

World Electric Vehicle Journal doi: 10.3390/wevj17050242

Authors: Michael Duoba Jorge Pulpeiro González

The SAE J2841 utility factor (UF) estimates the fraction of driving expected to occur in charge-depleting (CD) mode for plug-in hybrid electric vehicles. Emerging in-use data suggest that real-world electric usage is lower than assumed, motivating a reassessment of how charging behavior and related factors should be incorporated into the UF curve. Using trip-level data from approximately 1000 PHEVs observed over one year, we develop a charging model that captures both population-level heterogeneity in charging frequency and day-to-day characteristic temporal patterns in individual charging. The charging behavior modeling is applied to NHTS driving data to generate UF curves spanning 5 to 200 miles (8 to 322 km) of CD range. When key behavioral features are included, the resulting CD driving fractions align closely with industry-provided data. Sensitivity analysis indicates that the assumed share of habitual non-chargers is among the most influential parameters affecting the gap between the original UF and in-use data. Multiple modeling approaches were used to explore the problem and compare results, including machine learning, logistic regression, and parametric methods. Additional factors such as blended CD operation and temperature effects are discussed within a modular framework for refining J2841. These findings inform ongoing discussions on PHEV utility representation in analytical and regulatory contexts.

World Electric Vehicle Journal doi: 10.3390/wevj17050241

Authors: Chia-Kai Wen Chia-Sheng Tsai

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators, which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint.

World Electric Vehicle Journal doi: 10.3390/wevj17050240

Authors: Krisztian Horvath

Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking internal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle refinement. This review synthesizes the current state of the art in harmonic NVH engineering for electric drivetrains, focusing on the interactions between gear geometry, manufacturing variability, electromechanical coupling, structural transfer, and human sound perception. Classical mechanisms of gear-mesh excitation are revisited together with emerging EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The review further examines recent progress in predictive and data-driven approaches, including machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary system-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review provides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains.

World Electric Vehicle Journal doi: 10.3390/wevj17050239

Authors: Joe Dahrouj Sadaf Hussain Alessandro Giannetti Davide Tarsitano

The electrification of regional railway lines using battery-electric trains requires accurate simulation tools to support energy management and thermal control design. This paper presents an integrated dynamic simulation model of the traction system of a Hitachi Caravaggio ETR 521 regional train operating in battery-electric mode, developed in MATLAB/Simulink 2024b. The model incorporates all key drivetrain components, including a train reference generator, speed controller, motor controller, three-phase inverter, induction motor, a Kokam Co., Ltd. lithium-ion battery pack, and a detailed battery thermal management system. The proposed framework enables simultaneous evaluation of traction performance, battery state of charge (SOC) evolution, and thermal behavior under realistic conditions. To validate the model, simulations of the Treviso–Vicenza route were conducted under two scenarios: traction-only operation and operation with a 160 kW auxiliary load. Simulation results demonstrate that auxiliary loads significantly affect energy consumption and battery thermal behavior, with energy consumption increased by 50%. The results highlight the importance of integrating thermal effects into energy management and sizing decisions for battery-electric regional trains. The developed model provides a practical tool for optimizing battery sizing, thermal management strategies, and overall energy performance, supporting the planning and design of sustainable electric railway solutions. The modular MATLAB/Simulink architecture is designed to be route-agnostic; extension to other regional lines with different gradients, speed profiles, or extreme climate conditions (e.g., alpine routes or high-temperature regions) requires only updated route data and adjusted ambient boundary conditions, demonstrating the model’s broad applicability beyond the Treviso–Vicenza case study.

World Electric Vehicle Journal doi: 10.3390/wevj17050238

Authors: Marcell Misznéder Ulrich Rengstl Manuel Hopp-Hirschler Ulrich Nieken

Thermal management is a key factor for the efficiency, performance, and reliability of battery electric vehicles (BEVs), particularly in systems with strongly coupled components and heterogeneous thermal dynamics. This study proposes a centralized nonlinear model predictive control (NMPC) strategy for component cooling in BEVs, designed to maintain temperatures within optimal ranges while minimizing energy consumption and respecting actuator constraints. A reduced-order physics-based model is developed in MATLAB/Simulink R2024b, and the NMPC is implemented using CasADi, incorporating coolant temperatures as stabilizing states and a systematic parametrization of sampling time, prediction horizon, and weighting factors. The considered thermal management system consists of hydraulically coupled subsystems with different overall time constants, for which a single-horizon NMPC formulation is applied. Simulation results show that the proposed controller accurately tracks thermal dynamics across components with varying inertia and effectively captures cross-coupling effects. Sensitivity analyses indicate that variations in sampling time and prediction horizon have a limited impact on temperature trajectories and energy consumption, demonstrating robustness and real-time applicability. Compared to a rule-based controller, the NMPC achieves up to 30% reduction in energy consumption depending on ambient conditions and driving cycles, while improving temperature regulation, particularly for the high-voltage battery, with up to 2 K lower peak temperatures and a more balanced temperature distribution. These findings demonstrate that centralized NMPC is a suitable and efficient approach for thermal management in directly coupled BEV subsystems with heterogeneous dynamics.

World Electric Vehicle Journal doi: 10.3390/wevj17050237

Authors: Yi-Wen He Xiao-Ci Huang

A novel facial landmark localization method, termed TSDM, is proposed by integrating T-shaped features with the Supervised Descent Method (SDM). Facial landmark localization is critical for driver fatigue and attention detection in intelligent cockpits. Traditional methods lack accuracy and robustness in complex in-cabin environments such as varying illumination and head pose changes, while deep learning approaches are computationally expensive on resource-constrained vehicle platforms. The T-shaped feature well matches facial geometry and enhances feature representation. T-shaped features are selected via AdaBoost for robust face detection, and SDM is then used to locate 68 facial landmarks. Experiments show that TSDM achieves higher accuracy, lower false-positive rates, and better efficiency than traditional methods, including Haar and LBPH. It also exhibits stronger robustness and better real-time performance than several lightweight deep learning models (such as 3D-aware methods and SAN) on CPU-only platforms, while achieving comparable or higher localization accuracy. Experimental results show that TSDM achieves a face detection rate of 97.43% and a normalized mean error (NME) of 3.4% on standard datasets. The proposed method provides a practical solution for driver state monitoring in resource-limited vehicular environments.

World Electric Vehicle Journal doi: 10.3390/wevj17050236

Authors: Long Wu Yang Wang Likun Xing

As an important state parameter in battery management systems, accurate state of charge (SOC) estimation is of great significance for the safe and reliable use of batteries. In this paper, a Temporal Convolutional Network–Transformer (TCN–Transformer) model is proposed for achieving accurate estimation of SOC. First, the TCN is integrated in series with the Transformer model. This integration not only extracts the local characteristics of time-series data but also captures broader spatiotemporal correlations, thereby enhancing the feature representation and achieving highly accurate estimation. However, since the hyperparameter settings of neural networks have a significant impact on model performance, this study employs the advanced hippo optimization (HO) algorithm to determine the optimal values for the number of filters, filter size, number of residual blocks, and number of encoder layers, ultimately improving the model’s stability and efficiency. Finally, the proposed model was tested under various dynamic driving conditions at different temperatures. Experimental validation on the CALCE dataset demonstrates that the proposed HO–TCN–Transformer achieves RMSE and MAE both under 0.7%, representing an approximately 50% overall error reduction compared to the standalone TCN. Cross-validation across five folds confirms robust performance with <7% standard deviation.

World Electric Vehicle Journal doi: 10.3390/wevj17050235

Authors: Jasem Alazemi Jasem Alrajhi Ahmad Khalfan Khalid Alkhulaifi

In the original publication [...]

World Electric Vehicle Journal doi: 10.3390/wevj17050234

Authors: Fei Lai Dongsheng Jiang

To address the challenge of balancing regenerative braking efficiency and braking safety in rear-wheel-drive electric motorcycles, this study proposes a genetic algorithm-based electromechanical hybrid fuzzy braking control strategy. First, a three-dimensional fuzzy controller is designed with braking force, motorcycle speed, and battery state of charge (SOC) as input variables to adjust the regenerative braking ratio in real-time. To further improve the fuzzy logic, which typically relies on engineering experience, a genetic algorithm (GA) is employed to optimize the controller’s parameter space. Co-simulation results using BikeSim 2013.1 and MATLAB/Simulink R2022a demonstrate that, under WMTC and NEDC standard driving cycles, the proposed GA-optimized fuzzy control system increases energy recovery rates by 6.59% and 11.65%, respectively, compared with the unoptimized fuzzy control strategy.

World Electric Vehicle Journal doi: 10.3390/wevj17050233

Authors: Erik Skeel Ari Hentunen Mikko Pihlatie Jari Vepsäläinen Mikaela Ranta Prashant Singh Sai Santhosh Tota

Decarbonizing heavy-duty road freight transportation requires efficient energy management in zero-emission powertrains. This study investigates energy management strategies (EMSs) for a heavy-duty Fuel Cell Plug-in Hybrid Electric Vehicle (FC-PHEV). Rather than the typical charge-sustaining operation, these strategies are designed for charge-depleting operation, in which each route begins with a charged battery and ends at a lower state of charge (SOC), leveraging the vehicle’s plug-in capability. The EMSs are evaluated primarily in terms of energy consumption, while battery C-rate and fuel cell ramp rate are used as simple stress indicators for comparative analysis. A backward-facing vehicle model is developed to test several EMSs, including both optimization- and rule-based strategies. The Equivalent Consumption Minimization Strategy (ECMS) emerged as a promising option, motivating further testing with a forward-facing model and additional drive cycles. The simulation results show that ECMS consumed only 1.1% more energy than the global optimal solution found by Pontryagin’s Minimum Principle (PMP) and 7.5% less energy than a simple rule-based strategy, on average across five drive cycles. These results show that ECMS can be effective for a heavy-duty FC-PHEV operating in charge-depleting mode, extending its demonstrated applicability beyond charge-sustaining and light-duty vehicles.

World Electric Vehicle Journal doi: 10.3390/wevj17050232

Authors: Tanya Jeimiel T. Base Ardvin Kester S. Ong Maela Madel L. Cahigas Ma. Janice J. Gumasing

In the original publication [...]

World Electric Vehicle Journal doi: 10.3390/wevj17050231

Authors: Xiangya Qin Zefu Tan Qingshan Xu Li Cai Xiaojiang Zou Nina Dai

Series-connected lithium-ion battery packs are widely used in electric vehicles (EVs). However, inevitable inconsistency among cells can cause charge imbalance, accelerated aging, and reduced system safety. To improve the consistency of series-connected battery packs under complex EV operating conditions, this study proposes a hierarchical active balancing system. Bidirectional Buck–Boost converters are employed for intra-group balancing, and distributed flyback converters are used for inter-group balancing. A multi-stage coordinated balancing control strategy is further developed to reduce control complexity and improve balancing efficiency. A 16-cell series-connected battery pack model is established in MATLAB R2024a/Simulink and evaluated under resting, charging, and discharging conditions. The results show that, compared with the conventional single-layer Buck–Boost balancing topology, the proposed method reduces the balancing time by 58.09%, 57.97%, and 58.06%, respectively. These results indicate that the proposed system can effectively improve the consistency and balancing performance of series-connected battery packs, providing a scalable solution for EV battery management systems.

World Electric Vehicle Journal doi: 10.3390/wevj17050230

Authors: Ruochen Wang Feng Wei Renkai Ding Zhengrong Chen Wei Liu Dong Sun

To address the issue of vehicle instability and veering during braking when a single wheel fails in an electric vehicle’s electromechanical braking (EMB) system, an integrated application-oriented control framework based on adaptive sliding mode control (ASMC) is proposed. To address the shortcomings of SMC—such as difficulty in suppressing oscillations and the high workload associated with parameter tuning—a novel composite reaching law function was designed, and the TD3 algorithm was employed to optimize the sliding mode control parameters. When a failure in the EMB system is detected, the upper-layer control uses an improved ASMC algorithm to calculate the vehicle’s additional yaw moment. The lower-layer control employs an optimal control algorithm to distribute braking force, taking into account braking intensity, yaw moment, and tire utilization. This approach is integrated with sliding mode steering control to enhance vehicle stability during braking. To meet the driver’s braking requirements, a backpropagation (BP) neural network is first employed to identify braking intent. Based on this, the additional yaw moment is calculated by the upper-layer controller, and the brake force distribution is optimized through the lower-layer controller, thereby improving the vehicle’s stability. Through co-simulation analysis using Simulink-2024a and CarSim-2019.1, the results show that, compared to traditional algorithms, the proposed hierarchical control strategy reduced the maximum sideslip angle by 51.4%, decreased the maximum yaw rate by 47.2%, and reduced the maximum lateral offset by 45.6%. This control strategy enables enhanced stability across various braking intensity conditions.

World Electric Vehicle Journal doi: 10.3390/wevj17050229

Authors: Sara J. Ríos Elio Sánchez-Gutiérrez Síxifo Falcones

In recent years, the rapid expansion of electric vehicle (EV) charging infrastructure and the increasing penetration of renewable energy sources require highly efficient and dynamically robust power electronic interfaces. In photovoltaic (PV)-assisted EV charging stations and DC microgrids, bidirectional DC-DC converters (BDCs) are essential for managing power flow between PV arrays, battery energy storage systems, and the DC bus supplying EV chargers. This paper presents a novel voltage and current control design for a BDC operating in a PV-powered DC microgrid oriented to EV charging applications. Following a detailed mathematical model of the converter, a digital current controller and a predictive voltage regulator were developed using Model-Based Predictive Control (MBPC). The proposed cascade control structure enables accurate DC bus voltage regulation and seamless bidirectional power flow under dynamic load variations representative of EV charging and discharging scenarios. The control scheme was evaluated in MATLAB/SIMULINK® and experimentally validated through Field-Programmable Gate Array (FPGA)-based test benches using an OPAL-RT real-time (RT) simulator, integrating the RT-LAB and RT-eFPGAsim environments. The predictive controller achieved precise regulation in both buck and boost modes, reaching efficiencies of 97.07% and 98.57%, respectively. The results demonstrate that integrating MBPC with RT validation provides high performance, fast dynamic response, and computational efficiency, making the proposed approach suitable for renewable-integrated EV charging stations and next-generation DC microgrid-based mobility systems.

World Electric Vehicle Journal doi: 10.3390/wevj17050228

Authors: Junming Huang Jiayao Mao Rong Yang Pinpin Qin Lei Ye Wei Huang

To address the significant degradation of steering performance in dual-motor steer-by-wire (DMSBW) systems caused by the coupling of communication delays and motor faults, a robust fault-tolerant control strategy is proposed under the dual-motor collaborative driving mode. First, a matrix polytopic model is employed to describe the nonlinearities introduced by delays, establishing a delay-dependent DMSBW system dynamics model. Second, for electrical faults such as internal motor short circuits that cause a sudden drop in rotational speed, an adaptive motor-switching fault-tolerant mechanism is designed based on a smooth monitoring function to achieve rapid fault detection and steering function reconstruction. Furthermore, considering the coupled impact of delays and faults, a robust linear quadratic regulator (LQR) controller with feedforward compensation is designed to enhance system fault tolerance and robustness. Simulation results demonstrate that under steering wheel angle step input with delays, the proposed strategy achieves a rapid response without significant overshoot, and the steady-state tracking error is significantly reduced. In variable-speed single lane change maneuvers with coupled delays and severe motor faults, the peak and root mean square (RMS) errors of the front wheel angle are reduced to 0.0112 rad and 0.0031 rad, respectively. Compared to the delay-compensated nonlinear model predictive control (NMPC) and sliding mode control (SMC) strategies that do not account for delays, the peak error is reduced by 15.79% and 45.37%, while the RMS error decreases by 27.91% and 35.42%, respectively. Additionally, the peak and RMS errors of the sideslip angle and yaw rate are substantially reduced, validating the strategy’s excellent steering fault tolerance, robustness, and vehicle handling stability.

World Electric Vehicle Journal doi: 10.3390/wevj17050227

Authors: Kashif Raza Maitane Berecibar Md Sazzad Hosen

Electric vehicles are becoming more common daily because countries are moving towards net-zero emissions. Different generations of NMC battery cells are used for EV applications. This work investigates the degradation behavior of high-energy 75 Ah prismatic NMC631 lithium-ion cells using a combined incremental capacity analysis (ICA) and electrochemical impedance spectroscopy (EIS) framework under different conditions. Cells are cycled at an identical C-rates and depths of discharge (DoD), and at different temperatures to systematically evaluate the impact of temperature on electrochemical aging. ICA results revealed that cells cycled at low temperatures maintain stable peaks and a high SoH (>90%) after completing 1600 full equivalent cycles (FECs). EIS analysis confirms the distinct impedance evolution patterns. Degradation mode analysis is performed using the ICA, and EIS highlights the combined evolution of conductivity loss, loss of lithium inventory, and loss of active material. It also highlights different degradation path trajectories under identical operating conditions stem from the progressive amplification of internal cell heterogeneities during aging. The results demonstrate that combining ICA and EIS provides complementary insights into degradation evolution and enables clear differentiation between gradual aging and sudden failure pathways in high-energy NMC cells.

World Electric Vehicle Journal doi: 10.3390/wevj17050226

Authors: Andreas Daberkow Barbara Wild

Addition of Institutional Review Board Statement and Informed Consent Statement in back matter [...]

World Electric Vehicle Journal doi: 10.3390/wevj17050224

Authors: Klara Schlüter Erlend Grytli Tveten Severin Sadjina Brage Bøe Svendsen Anne Bruyat Olve Mo

We present a parametrised charging infrastructure model developed to support the design of a hybrid electric zero-emission vessel with corresponding charging infrastructure for operation along the Norwegian Coastal Express route. The charging model includes functionalities to analyse the required battery storage capacity and power ratings and locations of charging facilities for achieving battery-electric operation. We demonstrate the use of the charging model to analyse different zero-emission scenarios for the Norwegian Coastal Express route. In the presented example scenarios, the model takes as input the estimated energy demand for a new zero-emission vessel design for the Coastal Express in different weather conditions, and includes functionality to consider realistic port stays based on existing timetables and historical data of delays. The analyses show minimal required battery capacities and illustrate a trade-off between charging power and battery capacity, as well as exemplifying the impact of different timetables and historic deviations on charging and energy delivered from the battery. The charging model presented is general and can be used for other routes than the Norwegian Coastal Express, as a tool for decision-makers to optimize for battery-electric operation whilst keeping the need for onboard storage capacity and charging infrastructure installations at a minimum.

World Electric Vehicle Journal doi: 10.3390/wevj17050225

Authors: Yipu Xu Xia Liu Chengsong Li Wenjun Chen Jiatong Deng

Wide-bandgap power devices, particularly silicon carbide (SiC) MOSFETs, have seen widespread adoption in electric vehicle (EV) motor drive systems due to their superior switching characteristics, including high switching speeds and high switching frequencies. However, these advantages exacerbate motor terminal overvoltage, with peaks reaching twice the inverter output voltage, causing insulation breakdown in windings and bearing electro-corrosion, which shorten motor lifespan. Traditional overvoltage prediction methods, such as distributed parameter models or detailed ladder network approaches, require extensive system parameters and involve high computational loads, while simplified models lack generality. To address these issues, this paper proposes a simplified prediction method based on a lumped ladder network model combined with frequency scanning. The approach uses impedance analysis to identify anti-resonance frequencies, enabling direct estimation of overvoltage amplitudes without prior knowledge of cable or motor specifics. Experimental validation on a SiC-based drive system demonstrates prediction errors below 10% and a reduction in computational time compared to conventional methods.

World Electric Vehicle Journal doi: 10.3390/wevj17050223

Authors: Yuan Mao Yuanzhi Wang Junting Bao Xiaofei Luo Youbing Zhang

Permanent magnet synchronous motors (PMSMs) have been widely used in new-energy vehicles and industrial servo systems. However, demagnetization faults (DMFs) can lead to severe issues, including torque ripple and magnetic field distortion. This paper proposes an intelligent diagnostic approach for DMFs based on stator tooth flux (STF). A mathematical model of STF is formulated, and the magnetic flux change is measured using multiple sets of anti-series-connected detection coils (DCs). By combining finite element simulation with signal processing technology, we establish a comprehensive diagnostic system covering fault feature extraction, fault location identification, and severity assessment is established. The proposed method employs wavelet transform (WT) to extract time-frequency features of voltage signals and combines it with a convolutional neural network (CNN) to form the WT-CNN intelligent diagnosis model. Based on the extracted voltage signal features, the method achieves intelligent identification and visual localization of DMFs. Simulation results show that the proposed method achieves an accuracy above 80% for fault location identification (defined as sample-level multi-label classification accuracy across 12 PMs) and above 85% for demagnetization severity estimation (defined as classification accuracy across 9 severity degrees from 10% to 90%). These results provide an effective technical foundation for motor condition monitoring and fault early warning in simulation environments.

World Electric Vehicle Journal doi: 10.3390/wevj17040222

Authors: Yueping Xiang Luoyi Li Yanqiu Hou Xiaoyu Dai Wenfeng Peng Zhuoyang Liu Ziming Liu Zicong Chen Xingyu Hu Lv He

To address the challenges faced by virtual power plants (VPPs) in uncertain market environments and complex distribution networks, including strong market coupling, difficulty in multi-resource coordination, and strict safety constraints, this paper proposes a Hierarchical Hybrid Intelligent Framework (H2IF). The proposed framework integrates a market-aware meta-game mechanism, a topology-embedded graph attention coordination method, and a risk-aware soft/hard constraint safety mechanism to achieve economically optimal dispatch of VPPs in complex dynamic scenarios. By explicitly modeling competitive market interactions, the proposed method enhances strategy robustness; by exploiting grid topology priors, it improves multi-agent coordination capability; and by combining differentiable projection with risk-constrained optimization, it jointly ensures operational safety and revenue stability. Simulation results on a modified IEEE 33-bus system demonstrate that H2IF outperforms mainstream deep reinforcement learning methods and rule-based dispatch strategies in overall performance. In the 24 × 300-step testing scenario, H2IF achieves an average single-episode operating cost of 38.23 k$, which is 28.9%, 40.4%, and 26.5% lower than those of MADDPG, SAC, and the rule-based method, respectively, while also yielding the lowest constraint violation level. Ablation studies further verify the effectiveness of each key module in improving profit, reducing operating costs, enhancing tracking performance, and strengthening safety. The results indicate that the proposed method enables coordinated optimization of economy, safety, and robustness for VPP dispatch under uncertain market and operating conditions.

World Electric Vehicle Journal doi: 10.3390/wevj17040221

Authors: Ziming Wang Mingyang Du Xuefeng Li Dong Liu Jingzong Yang

Driven by the global agenda of low-carbon urban development, local governments in China have implemented targeted policies requiring new energy vehicle adoption in the ride-hailing industry. This study focuses on a key issue in the development of sustainable smart public transportation systems: the factors affecting the acceptance of electric vehicles (EVs) in ride-hailing services among full-time and part-time drivers. Using 432 valid samples of ride-hailing drivers from Zhangzhou, a third-tier city in China, we compared the basic personal attributes of full-time and part-time drivers. Ordered logit models were developed to explore differences in factors influencing their acceptance of electric ride hailing (ER). Findings reveal: (1) Drivers’ perceived significance of EVs in green transportation is positively associated with their acceptance of ER. (2) Endurance mileage and charging efficiency have no significant effect on acceptance among drivers in underdeveloped cities. (3) Full-time drivers exhibit relatively low concern for subsidy policies, whereas part-time drivers express a pressing need for vehicle purchase subsidies and operational subsidies. (4) Overall, part-time drivers demonstrate higher acceptance of ER than full-time drivers. Based on these findings, this paper offers policy recommendations for governments to enhance ER acceptance among both driver groups. It is important to note that the present study utilizes survey data collected from Zhangzhou. The research conclusions should be treated with caution when applied to other cities, and further studies can be conducted in different regions to verify the results.

World Electric Vehicle Journal doi: 10.3390/wevj17040220

Authors: Flavio Calvi Antonella Accardo Henrique de Carvalho Pinheiro Giovanni Imberti Ezio Spessa Massimiliana Carello

The European Union is currently focused on reducing non-exhaust emissions (NEE), a growing source of particulate matter (PM) pollution from road transport. This study presents the Life Cycle Assessment (LCA) of an innovative zero-emission magneto-rheological braking system specifically designed to meet new brake emission targets. Prototyped for A-segment passenger cars, the system uses magnetorheological fluids that modify their rheological properties when subjected to an external magnetic field. The environmental impacts of this innovative system are compared with those of a conventional disc brake, considering 16 environmental indicators across all life stages: raw material extraction, manufacturing, use, and end-of-life. In fact, although the system eliminates PM emissions during operation, it is crucial to assess whether it remains advantageous in terms of overall environmental impacts when the full life cycle is considered. As a prototype, this study also aims to inform design improvements that minimize environmental burdens. Results show that the innovative braking system performs better, particularly during the use and maintenance phases. Moreover, several eco-design strategies have been identified to reduce impacts related to materials and production. Overall, the magneto-rheological system demonstrates strong potential to meet future emission standards while improving the sustainability of vehicle braking technology.

World Electric Vehicle Journal doi: 10.3390/wevj17040219

Authors: Yiyong Pan Xilai Jia Jieru Huang Gen Li Pengyu Xu

Accurately estimating the severity of crash injuries among older drivers is paramount for enhancing traffic safety, a task challenged by class imbalance and label noise. Traditional predictive paradigms often struggle to identify rare severe cases, as they tend to prioritize global accuracy, thereby compromising sensitivity to high-risk outcomes. To overcome these limitations, this study develops a Log-Loss Cleaned and Probability-Calibrated Cascade (L-CSC) framework by strategically integrating existing advanced algorithmic components for robust and reliable severity prediction. Initially, a Log-Loss-based noise filtering mechanism is implemented to purge outliers and ambiguous samples from the training data, thereby enabling higher-quality representation learning. Subsequently, a two-stage cascade architecture is designed to decouple the classification task. Stage I employs a Preliminary Screening Model, optimized via Bayesian optimization for F2-score, to specifically maximize the recall for severe and fatal cases. In Stage II, a Stacking ensemble classifier is deployed to achieve a fine-grained classification of injury levels among the cases identified in the initial screening. Finally, Isotonic Regression is employed to calibrate the output probabilities from both stages, ensuring that the resulting risk estimations are statistically sound and reliable. Empirical evaluations demonstrate that the L-CSC framework effectively balances overall performance with critical risk detection, achieving a robust Macro-F1 of 0.7296. Specifically, compared to the best-performing baseline, the recall and F1-score for the critical severe and fatal category showed relative improvements of over 82% and 62%, respectively. Ablation analyses further substantiate the vital contributions of both the data cleaning and calibration modules. This research demonstrates that the cascaded framework effectively mitigates the biases inherent in imbalanced datasets, providing a robust algorithmic foundation to potentially support future traffic safety interventions.

World Electric Vehicle Journal doi: 10.3390/wevj17040218

Authors: Fanlong Zeng Tingting Chen

Power battery enterprises are a key link in the new energy vehicle (NEV) industry chain. However, studies analyzing the investment layout of power battery enterprises from a micro perspective are relatively scarce. This study takes Contemporary Amperex Technology Co. Limited (CATL) as a case and employs various spatial analysis methods and an optimal parameter-based geographical detector (OPGD) to analyze the spatiotemporal evolution and driving mechanisms of its investment layout from 2020 to 2024. The results indicate that CATL’s investment center has shifted from Jiangxi to Hubei, and the spatial expansion axis has changed from a northwest–southeast to a southwest–northeast direction. The investment layout has evolved from a “one core with two secondary cores” structure to a “provincial dual core, multi-core outside the province” structure and, ultimately, to a nationwide networked pattern. By 2024, CATL’s investment network covered the southeastern coast, the Yangtze River Delta (YRD), the Pearl River Delta (PRD), central China, and southwestern regions. County-level spatial autocorrelation analysis shows that the investment agglomeration effect has continuously strengthened (with the global Moran’s I increasing from 0.006 to 0.025). High–high agglomeration areas gradually expanded from the southeastern coast to Xiamen and several provinces in central and western China, while high–low agglomeration areas, as early signals of investment diffusion, initially expanded and then contracted. The driving mechanism analysis reveals that fiscal support (q = 0.668), industrial structure upgrading (q = 0.585), tax burden (q = 0.543), and economic development (q = 0.536) are the primary factors driving investment layout, with significant synergistic effects between these factors. The synergy between industrial structure upgrading and clean energy supply stands out as particularly prominent. These findings contribute to optimizing the spatial layout of the NEV industry and promoting regional economic development.

World Electric Vehicle Journal doi: 10.3390/wevj17040217

Authors: Camila Minchala-Ávila Paul Arévalo-Cordero Danny Ochoa-Correa

High renewable penetration reduces effective inertia and increases frequency variability in microgrids, thereby limiting the performance of purely reactive frequency regulation. This paper presents a two-timescale frequency-support strategy based on bidirectional electric vehicles. The main novelty lies in introducing a learning-assisted correction layer between forecast-based aggregate regulation and final EV-level dispatch. Rather than replacing the predictive controller with an end-to-end data-driven policy, this layer uses measured fleet-state information to correct the supervisory aggregate request online before a final feasibility-preserving dispatch stage converts it into executable vehicle-level commands under concurrent power, energy, plug-in, and departure constraints. A supervisory predictive layer determines the aggregate support action from forecasted photovoltaic and load disturbances, whereas a lower real-time dispatch layer redistributes that action across the available fleet. Feasibility is enforced through an explicit projection stage prior to actuation. The method is assessed in simulation using measured campus operating profiles of irradiance, temperature, demand, frequency, and electric-vehicle availability. Across four representative operating days, the proposed strategy reduced the mean cumulative frequency deviation by 30.3% relative to droop control and by 24.7% relative to predictive-only operation, while reducing the mean time outside the admissible frequency band by 22.2% and 20.0%, respectively. Zero post-projection constraint violations were observed in all evaluated cases. These gains were obtained at the expense of higher actuation usage, thereby making the regulation–usage trade-off explicit.

World Electric Vehicle Journal doi: 10.3390/wevj17040216

Authors: Dario Barri Federico Soresini Giacomo Guidotti Pietro Agostinacchio Federico Maria Ballo Massimiliano Gobbi

With the growing demand for hybrid and electric vehicles, the accurate prediction of NVH (Noise, Vibration, and Harshness) behavior in Permanent Magnet Synchronous Machines (PMSMs) has become a critical aspect of electric motor design. This paper presents a detailed modeling approach for electromagnetic-induced noise and vibrations in PMSMs, integrating both analytical and numerical methods. The model focuses on quantifying the contributions of radial and tangential electromagnetic forces, which are key drivers of vibro-acoustic responses. The analytical part employs curved beam theory and a simplified acoustic model, offering rapid insights during early design stages. In parallel, a detailed numerical model based on finite element analysis is developed using a physics-based approach that accounts for the actual geometry and material properties of the PMSM prototype. This allows for enhanced accuracy without relying on experimental material parameter identification. Moreover, the detailed model includes the fluid–structure interaction introduced by the channels of the cooling fluid of the electric machine, which, although poorly addressed by the existing literature, was found to play a key role in driving the vibrational behaviour of the structure. By combining analytical speed with numerical precision, the proposed approach enables consistent and physically-based NVH predictions across various design phases, ultimately supporting improved electric machine performance and reducing development time and costs. Validation against experimental data confirms the ability of the model to accurately predict both sound pressure levels and housing surface vibrations. The novelty of this work lies in its integration of fluid–structure interaction and material modeling without the need for empirical parameter tuning, offering a robust tool for NVH design in electric vehicle applications.

World Electric Vehicle Journal doi: 10.3390/wevj17040215

Authors: Dener A. de L. Brandao Thiago M. Parreiras Igor A. Pires Braz J. Cardoso Filho

Extreme fast charging (XFC) infrastructure is becoming increasingly necessary as the number of electric vehicles continues to grow. However, deploying such stations introduces several challenges related to power quality and compliance with regulatory standards. This work presents an alternative XFC station designed for charging multiple vehicles while ensuring low harmonic distortion in the grid currents, without the need for sinusoidal filters, by employing the Zero Harmonic Distortion (ZHD) converter. The proposed system offers galvanic isolation for each charging interface and supports additional functionalities, including the integration of Distributed Energy Resources (DERs) and the provision of ancillary services. These features are enabled through the combination of a bidirectional grid-connected active front-end operating at low switching frequency with high-frequency silicon carbide (SiC)-based dc/dc converters on the vehicle side. Hardware-in-the-loop (HIL) simulation results demonstrate a total demand distortion (TDD) of 1.12% for charging scenarios involving both 400 V and 800 V battery systems, remaining within the limits specified by IEEE 519-2022.

World Electric Vehicle Journal doi: 10.3390/wevj17040214

Authors: Zhang Ni Weihong Wang Jingyi Gu Zhi Li Bo Li

For electric vehicles, accurate real-time estimation of the road friction coefficient is critical for maintaining stability, as the millisecond-level response of electric motors and the integration of regenerative braking demand higher perception fidelity than traditional internal combustion vehicles. This paper proposes a methodological framework for road friction estimation specifically designed for intelligent E-Chassis based on micro-signal features of intelligent tires and deep learning. An intelligent tire system, integrated with tri-axial accelerometers and strain gauges, was installed on the front-left wheel of a test vehicle to capture raw dynamic signals during transitions from cement to snow-covered surfaces across a velocity gradient of 10–50 km/h. The Savitzky–Golay convolutional smoothing algorithm was applied to reconstruct the high-frequency raw signals, enabling the extraction of a five-dimensional feature vector comprising vehicle velocity, peak strain, contact patch width, peak-to-peak acceleration, and signal standard deviation. The study revealed a natural filtering effect originating from the porous elastic properties of snow, resulting in a 60–70% reduction in signal standard deviation compared to cement, accompanied by a cliff-like feature collapse at the moment of snow entry. A BP neural network model with a 5-7-1 architecture achieved an identification accuracy of 96.2% on the test set, facilitating a rapid real-time prediction of the friction coefficient transitioning from 0.75 to 0.23. Unlike traditional methods, the proposed approach does not rely on high slip ratios and can complete identification within the first physical rotation cycle. This provides a robust physical criterion for the torque vectoring and regenerative braking stability of intelligent electric vehicles in extreme environments.

World Electric Vehicle Journal doi: 10.3390/wevj17040213

Authors: Jinbiao Shi Weibo Zheng Ran Huo Po Hong Bing Li Pingwen Ming

Addressing the lack of high-fidelity test cycles for fuel cell light-duty trucks, this paper proposes an automated driving cycle construction method that integrates unsupervised clustering and reinforcement learning. Firstly, based on large-sample real-world driving data, four libraries of typical driving pattern segments are extracted through dimensionality reduction via Principal Component Analysis (PCA) and K-means clustering. Subsequently, the cycle construction process is formulated as a sequential decision-making problem, and a framework based on the Proximal Policy Optimization (PPO) algorithm, incorporating an action masking mechanism, is designed. This framework innovatively injects macro-level time budget allocation as a hard constraint into the agent’s policy space via action masking, while utilizing micro-level Markov transition probabilities as a soft guide. This dual approach drives the agent to learn an optimal segment concatenation strategy, thereby simultaneously ensuring both the macro-level statistical representativeness and the micro-level driving logic coherence of the synthesized cycle. Validation results demonstrate that the cycle constructed by the proposed method achieves an average relative error of only 7.53% in key characteristic parameters, and its joint speed-acceleration distribution exhibits a similarity as high as 0.9886 with the original data, significantly outperforming traditional methods such as the clustering method, the Markov chain method, and standard driving cycles. This study provides an effective tool for generating high-fidelity driving cycles and testing energy management strategies for fuel cell commercial vehicles.

World Electric Vehicle Journal doi: 10.3390/wevj17040212

Authors: Ningning Wei Lei Huo

This study investigates how the initial oxygen fraction in a confined space affects post-vent combustion, gas composition, and pressure hazards during thermal runaway (TR) of 58 Ah prismatic Li(Ni0.8Co0.1Mn0.1)O2 lithium-ion cells. Thermal abuse experiments were conducted in a 250 L sealed chamber under five initial oxygen fractions (20%, 15%, 10%, 5%, and 0% O2), with synchronized measurements of cell temperature, vent-jet temperature, chamber pressure, voltage, and post-event gas composition. A first-vent event occurred reproducibly at a cell surface temperature of approximately 155 °C, followed by TR onset at about 170 °C. Although the onset temperatures were only weakly affected by ambient oxygen concentration, the post-vent hazard escalation depended strongly on oxygen availability. As the initial oxygen fraction increased from 0% to 20%, the peak vent-jet temperature increased from 353 °C to 1172 °C, and the peak chamber pressure rose from 90.7 kPa to 523.1 kPa. Gas chromatography showed that H2, CO2, CO, CH4, and C2H4 were the dominant gaseous products. Lower oxygen fractions promoted retention of combustible species, whereas higher oxygen fractions enhanced oxidation and increased the CO2/CO ratio. An oxygen-participation parameter, η, was introduced to quantify the fraction of initially available chamber oxygen consumed during post-vent oxidation. The increase in η was positively associated with oxygen-involved heat release and chamber overpressure. When the accessible oxygen fraction was limited to 10% or below, secondary combustion and pressure buildup were markedly suppressed, although a localized near-field thermal hazard remained significant around 10% O2. These results provide quantitative guidance for enclosure inerting, vent management, and post-vent hazard mitigation in high-energy lithium-ion battery systems.

World Electric Vehicle Journal doi: 10.3390/wevj17040211

Authors: Cagri Un

This study aims to show a detailed life cycle assessment (LCA) approach of battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs), with an emphasis on determining the electrical carbon intensity at which these vehicles reach life-cycle greenhouse gas (GHG) parity. The analysis was conducted in openLCA v2.0.3 using the Ecoinvent v3.9.1 database under a European use-phase context, with a functional unit of 150,000 km. BEVs were evaluated for two representative lithium-ion battery chemistries (NMC622 and LFP) under three electricity carbon intensity scenarios (50, 400, and 850 g CO2/kWh), while ICEVs were modeled for both gasoline and diesel pathways. Results show that BEV life-cycle GHG emissions vary between 91 and 221 g CO2-eq/km across different combinations of electricity mix, battery chemistry, and end-of-life conditions. When isolating electricity carbon intensity as the primary variable under a fixed BEV configuration, emissions increase approximately linearly with grid emission factor. Under average European electricity conditions (400 g CO2/kWh), BEVs exhibit lower life-cycle GHG emissions than gasoline ICEVs, whereas under coal-intensive electricity conditions (850 g CO2/kWh) this advantage may be reduced or reversed. The break-even electricity carbon intensity is derived by linear interpolation under a fixed BEV configuration (NMC622, 60 kWh, constant lifetime and EoL conditions), yielding a threshold of approximately 600 g CO2/kWh. The results further indicate that this threshold is influenced by battery chemistry, production-related emissions, recycling efficiency, and assumed vehicle lifetime. These findings highlight the importance of simultaneous progress in electricity decarbonization and end-of-life recycling to secure the environmental benefits of vehicle electrification, and they provide a threshold-oriented framework for policy-relevant interpretation of comparative vehicle LCA results.

World Electric Vehicle Journal doi: 10.3390/wevj17040210

Authors: Yue Wang Yu Wang Shiqi Liu Yanpeng Zhu Bo Wang Yixin Li Guoqun Yao Wei Zhong

Existing energy consumption models suffer from accuracy degradation and limited robustness in complex urban environments due to insufficient consideration of the route spatiotemporal characteristics of electric buses. To address this limitation, a Time-Partitioned Dual-Layer LSTM (TP-D-LSTM) framework driven by cloud data and spatiotemporal characteristics is proposed. First, a spatiotemporal characteristics analysis is conducted on urban bus routes to reveal the underlying traffic flow dynamics. Based on these insights, a time-partitioning strategy is developed to classify the continuous operating data into independent periods while preserving the kinematic continuity of individual trips. Subsequently, a Dual-Layer LSTM (D-LSTM) is constructed to precisely capture the distinct energy consumption mechanisms within each partitioned scenario. Experiments based on real-world cloud-logged data demonstrate that the proposed TP-D-LSTM framework is superior to existing baseline models. By alleviating the limitations of global mixed modeling, the TP-D-LSTM significantly reduces the Root Mean Square Error (RMSE) to 6.15, achieving an improvement of over 50% compared to the D-LSTM, and exhibits remarkable stability under highly volatile traffic conditions.

World Electric Vehicle Journal doi: 10.3390/wevj17040209

Authors: Fang Xie Bichen Li Fu Han Lianghu Mao Yefan Yang

The development of new energy vehicles (NEVs) is key to the green and high-quality upgrading of China’s automotive industry, with the penetration rate of domestic NEV passenger cars exceeding 50%. However, deepening industry–university–research (IUR) collaborative innovation to break core technological bottlenecks remains a critical challenge. To address the limitations of existing studies—mostly focusing on dyadic interactions or hypothetical numerical simulations—this study constructs a novel tripartite evolutionary game model of NEV enterprises, university–research institutions, and the government, fully incorporating the industry’s unique attributes of high technological complexity, industrial integration, and innovation risk. Innovatively, we calibrate and verify the model using actual operation data from the Yancheng Institute of Technology–Yueda New Energy Vehicle College, bridging the gap between traditional theoretical simulation and industrial practice. The quantitative findings show that: a 40–60% balanced benefit distribution and matching cost-sharing mechanism are the core conditions for the system to reach an evolutionarily stable state; when the achievement transformation coefficient exceeds 50%, the convergence rate of stable cooperation willingness between both parties increases by over 40%; a moderate government subsidy intensity of 55% effectively accelerates the system’s positive evolution, with the incentive effect of subsidies diminishing rapidly in the mature collaboration stage; and robust collaborative innovation technology can reduce government intervention demand by more than 60%. This study enriches the theory of NEV IUR collaborative innovation, breaks the limitations of traditional research frameworks, and provides actionable references for promoting the high-quality development of the NEV industry.

World Electric Vehicle Journal doi: 10.3390/wevj17040208

Authors: Liqiao Yang Congcong Li

The electric vehicle (EV) battery industry has become a strategic pillar of the low-carbon transition, with far-reaching implications for industrial competitiveness and sustainability. This paper compares the policy mixes governing EV batteries in the EU and China and examines how different approaches shape technological innovation, industrial development, and export performance. A qualitative comparative case study is conducted, combining content analysis of core policy and regulatory documents with descriptive indicators on EV deployment, patenting activity, manufacturing capacity, and international trade. The analysis identifies two distinct but partly complementary policy models. The EU relies on innovation-driven and regulation-based instruments, coupling large research and development programs with stringent sustainability and circular-economy requirements; this model is associated with stronger performance in regulatory upgrading, collaborative innovation, and sustainability-oriented governance. China emphasizes demand expansion, large-scale fiscal support, and long-term industrial planning, which has accelerated capacity build-up, cost reductions, supply-chain integration, and manufacturing-based export competitiveness. The findings show that these contrasting policy mixes generate different technological trajectories and value-chain configurations, while both contribute to strengthening strategic competitiveness in the EV battery sector. More broadly, the study demonstrates that policy effectiveness depends less on any single instrument than on the coherence of the overall policy mix. It concludes that effective EV battery strategies should combine strong innovation incentives with mechanisms that support industrial scaling, supply-chain resilience, and high environmental standards.

World Electric Vehicle Journal doi: 10.3390/wevj17040207

Authors: M. Ziya Söğüt Zafer Utlu

The performance, safety, and long-term durability of electric vehicle (EV) battery systems are strongly governed by the chemical stability and thermophysical properties of their constituent materials. In response to the limitations of conventional lithium-based batteries—particularly with respect to thermal stability, material sustainability, and degradation under high operational loads—this study presents a thermodynamic and electrochemical modeling framework for evaluating alternative battery materials relevant to electric vehicle energy storage systems. Xenon difluoride (XeF2) and zirconium carbide (ZrC) are proposed as functional battery components and comparatively analyzed based on chemical stability, bond enthalpy, mass–capacity relationships, and energy density characteristics. Analytical modeling is employed to investigate voltage–capacity–mass interactions over a wide operating range (3–48 V and 100–1000 mAh), representing diverse EV operating scenarios, including high-load and elevated-temperature conditions. In addition, temperature-dependent degradation behavior and cycle life performance are assessed using logarithmic degradation models and Arrhenius-based life cycle formulations. The results indicate that ZrC, with a high total bond enthalpy of 561 kJ mol−1, demonstrates superior energy density, reduced material mass requirements, and enhanced resistance to thermal degradation, making it particularly suitable for high-temperature and long-life EV battery applications. In contrast, XeF2 exhibits stable electrochemical performance under moderate temperature and capacity conditions but shows increased sensitivity to thermal effects at higher operating ranges, suggesting potential applicability in balanced-performance EV battery configurations. Overall, the proposed modeling framework provides a systematic approach for assessing alternative battery materials under electric vehicle-relevant operating conditions and offers guidance for future experimental validation, material selection, and battery design aimed at improving safety, durability, and sustainability in next-generation electric vehicle energy storage systems.

World Electric Vehicle Journal doi: 10.3390/wevj17040206

Authors: Liyou Xu Chunyuan Tian Sixia Zhao Yiwei Wu Xianzhe Li Yanying Li Jiajia Wang

To improve the anti-slip performance and energy-efficient torque coordination of four-wheel-drive articulated tractors operating in hilly and mountainous terrains, this study proposes an integrated control framework that combines a 7-DOF tractor dynamics model, a GA-optimized fuzzy slip-ratio controller, and a three-level dynamic torque allocation strategy. First, a control-oriented full-vehicle dynamics model is established by integrating tractor body dynamics, wheel rotational dynamics, and the Dugoff tire model. Then, a fuzzy slip-ratio controller is designed using the slip-ratio tracking error and its rate of change as inputs, and its key parameters are optimized using a genetic algorithm. On this basis, a three-level dynamic torque allocation strategy is developed to coordinate the four in-wheel motors according to wheel-load distribution and slip-related constraints. MATLAB/Simulink (version 2023a) simulations and hardware-in-the-loop (HIL) tests are carried out to validate the proposed strategy. Under the straight-line driving condition, the RMSE of the proposed GA-fuzzy controller is reduced from 0.02716 to 0.00897. Under the steering condition, the average RMSE is reduced from 0.02079 to 0.01003. In addition, under the torque-allocation validation condition, the average four-wheel RMSE is reduced from 0.29632 under equal torque allocation to 0.02159 under the proposed three-level dynamic torque allocation strategy. The results indicate that the proposed method can effectively maintain the slip ratio near its target value, suppress excessive slip and redundant torque output, and improve the anti-slip and energy-efficient performance of articulated tractors. More importantly, the study provides an integrated control framework that unifies GA-optimized slip regulation and three-level torque coordination specifically for four-wheel-drive articulated tractors.

World Electric Vehicle Journal doi: 10.3390/wevj17040205

Authors: Yuri Katsuba Mikhail Kochegarov Andrey Zalyubovsky Alexander Sivov Alexander Bazhenov

In the context of the rapid growth of the electric and hybrid vehicle fleet, ensuring the reliability, safety, and durability of traction lithium-ion battery packs has become a key scientific and engineering challenge. The technical condition of battery packs, characterized by such parameters as state of charge (SOC), state of health (SOH), and remaining useful life (RUL), directly affects vehicle performance and the total cost of ownership of electric vehicles. This review article systematizes and analyzes current approaches to assessing the technical condition of battery packs. Fundamental degradation mechanisms and factors are considered, including operational, thermal, and mechanical effects. A detailed analysis is presented for the three main classes of diagnostic methods: model-based approaches, data-driven approaches (machine learning and deep learning), and hybrid methods combining the advantages of the former two. Particular attention is paid to methods for early fault detection, thermal runaway prediction, and condition assessment based on real-world operational data. The article presents quantitative results demonstrating the accuracy and effectiveness of various algorithms and also discusses key challenges and promising research directions, such as the use of cloud platforms, digital twins, and explainable artificial intelligence methods.

World Electric Vehicle Journal doi: 10.3390/wevj17040204

Authors: Leonie Taieb Martin Neuwirth Haydar Mecit

The integration of electric mobility and energy systems has emerged as a key research domain in the transition toward sustainable energy and decarbonized transport, yet the literature is lacking systematic quantitative overviews of its scientific development. This study addresses this gap by conducting a bibliometric analysis of research activities across five domains central to electric vehicle–energy system integration: central energy management systems; renewable energy, hydrogen production, and large-scale storage; industrial applications; smart energy communities, virtual power plants, and vehicle-to-X; and urban high-power charging parks with local storage. Using publication data from Web of Science and Scopus, performance analysis and science mapping techniques were applied to examine publication dynamics, thematic structures, and intellectual linkages. Results indicate strong growth and consolidation around smart grids and decentralized flexibility solutions, particularly within energy management, renewable integration, and community-based energy systems, while industrial applications and high-power charging infrastructures remain comparatively underrepresented. The findings suggest a maturing interdisciplinary field characterized by expanding connections between mobility and energy research, alongside emerging opportunities related to industrial integration, charging infrastructure, and vehicle-to-grid deployment. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems, enabling a differentiated understanding of research dynamics. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems. The findings highlight priority areas for future research, particularly industrial integration and scalable charging infrastructure, and offer insights for policymakers and industry stakeholders.

World Electric Vehicle Journal doi: 10.3390/wevj17040203

Authors: You Zhou Yifei Li Jianquan Ouyang

In Inductive Power Transfer (IPT) systems, coils misalignment can significantly alter the system’s output power, thereby compromising operational stability. To address this issue, this article proposes a mode switching approach. Firstly, let the IPT system operate under conditions of primary-side inductive detuning and secondary-side resonance. Subsequently, by modifying the inverter’s switching frequency and conduction scheme, it is possible to vary the system’s operating frequency and the degree of primary-side detuning, thereby producing two distinct output power curves. Therefore, the controller can dynamically change the switching frequency of the inverter and the degree of primary-side detuning based on coil misalignment, thereby mitigating the effects of variations in the coupling coefficient on the output power by switching the output power curve. Finally, the parameter selection step and open-loop control system are presented. An experimental prototype with an output power of 600 W was constructed to validate theoretical analysis. The experimental results indicate that, when the system load is set to 10.4 Ω and the coupling coefficient varies between 0.23 and 0.50, the output power fluctuation of the system is 4.5%, and the transmission efficiency is 91.7%. The experimental results demonstrate that the proposed working mode switching method significantly improves the system’s anti-misalignment characteristics.

World Electric Vehicle Journal doi: 10.3390/wevj17040202

Authors: Tatjana Apanasevic Anna Fjällström

Urban freight transport generates significant negative externalities in the form of noise, congestion, and environmental impacts. Freight consolidation centres could be seen as a potential solution, offering benefits such as shorter delivery distances and fewer delivery routes. However, implementation of freight consolidation centers requires collaboration between actors with conflicting interests and goals. This study proposes a collaborative business model framework for freight consolidation centres. The novelty of the study lies in conceptualising collaboration as an outcome-based partnership and extending the Business Model Canvas with collaboration-specific components. This framework was empirically tested and refined through a pilot project in Gothenburg, applying the principles of engaged scholarship. The results indicate that last-mile consolidation can significantly improve operational efficiency and enable sustainability gains. At the same time, structural, economic, and organisational barriers need to be addressed to realise all benefits of the collaborative business model. The findings particularly highlight the need for deeper institutionalisation of collaborative practices, including the integration of new norms, procedures, and policies into the business models of the individual actors involved.

World Electric Vehicle Journal doi: 10.3390/wevj17040201

Authors: Zahra Tasnim Kian Lun Soon Wei Hown Tee Lam Tatt Soon Wai Leong Pang Sui Ping Lee Fazliyatul Azwa Md Rezali Nai Shyan Lai Wen Xun Lian

Conventional black-box models for electric vehicle (EV) battery State-of-Health (SOH) prediction achieve high accuracy but lack interpretability, limiting their practical deployment in Battery Management Systems (BMSs). To circumvent these limitations, this study proposes a novel Grey-Box Residual-Driven Framework (GBRDF) that synergizes Deep Symbolic Regression (DSR) with a residual-learning BiLSTM network with two contributions: (1) the DSR component derives explicit, interpretable mathematical expressions governing global degradation trajectories based on electrochemical features, and (2) the BiLSTM network models the residual errors to capture high-frequency nonlinearities and complex sequential dependencies not addressed by the symbolic baseline. By fusing the physics-informed transparency of DSR with the data-driven refinement of BiLSTM, the GBRDF significantly enhances forecasting precision. Experimental validation across four independent EV datasets shows that the GBRDF achieves the highest coefficient of determination (R2) of 0.982, and the lowest mean absolute error (MAE) of 0.1398 and root mean square error (RMSE) of 0.3176, significantly outperforming existing methods. Furthermore, the DSR-derived SOH equation shows that battery degradation is primarily driven by high voltage exposure and charging time, with mathematical transformations reflecting how degradation accelerates initially then slows, matching real-world aging patterns where voltage stress dominates over temperature and usage variations.

World Electric Vehicle Journal doi: 10.3390/wevj17040200

Authors: Xingze Wu Xiang Zhao Wen Zeng Cheng Li

Electric unmanned vehicles applied in complex terrains such as agricultural, forestry, and deep-space exploration scenarios are often required to travel on uneven roads. In particular, during climbing processes, their driving stability and terrain adaptability are of critical importance. To address the above challenges, an electric unmanned vehicle with variable-diameter wheels is proposed. By adjusting the wheel diameter, the vehicle can modify its pitch and roll angles to adapt to uneven terrains. The core research focuses on the relationship between quasi-static stability and wheel diameter variation. First, the configuration and working principle of the electric unmanned vehicle with variable-diameter wheels are introduced, with particular emphasis on the mechanism principle of the novel variable-diameter wheel. A kinematic model between the electric cylinder input and wheel diameter in the variable-diameter wheel is established. On this basis, based on the FASM (Force-Angle Stability Margin)—a stable cone theory, the relationships between stability and wheel diameter variation were investigated separately under lateral, longitudinal, and 45° steering composite conditions on a slope. The results indicate that the unmanned vehicle can achieve omnidirectional attitude adjustment. Finally, the relationship between the electric cylinder input and stability is derived, which can provide a theoretical basis for the quasi-static stability control of outdoor electric unmanned vehicles.

World Electric Vehicle Journal doi: 10.3390/wevj17040199

Authors: Shahid Jaman Amin Dalir Thomas Geury Mohamed El-Baghdadi Omar Hegazy

This paper presents a distributed V2G-enabled multiport DC charging system with a hierarchical charging management strategy. Unlike conventional architectures based on centralized power converter cabinets, the proposed system distributes bidirectional power converters within individual multiport dispensers, each equipped with a local charging power management device. This architecture improves system scalability, fault tolerance, and operational flexibility while enabling vehicle-level charging and V2G services. A hierarchical control framework is introduced, consisting of high-level optimal charging scheduling, mid-level power coordination among distributed dispensers, and low-level converter control. Key elements include modular power units that can be dynamically configured and expanded, providing a cost-effective and adaptable solution for growing EV markets. Experimental results obtained from a 45 kW modular DC charging prototype demonstrate an efficiency improvement of up to 2% at rated power compared to a non-modular charger. In contrast, the optimized charging strategy achieves an overall charging cost reduction of approximately 11% and a peak load demand reduction of up to 31%. Furthermore, stable bidirectional power flow, effective power sharing, and total harmonic distortion within regulatory limits are experimentally validated during both charging and V2G operation. The prototype is implemented to validate the proposed charging system in the laboratory environment.

World Electric Vehicle Journal doi: 10.3390/wevj17040198

Authors: Rong Yang Zhiqi Sun Jiajia Yang Song Zhang

To address the prominent fatigue failure risk of planetary gears in power-split hybrid buses and the lack of quantitative damage analysis across various operating modes in existing studies, this paper focuses on the front planetary gear set of a power-split hybrid bus. Based on a full-vehicle co-simulation model, loads under full operating conditions are decomposed into 11 operating modes, mode-switching loads are analyzed and extracted, and mode-decomposed and mode-switching fatigue loading spectra are compiled. Fatigue simulation is then conducted using Miner’s linear damage accumulation rule. Results show that the sun gear directly coupled to motor is the system’s most fatigue-susceptible component, exhibiting significant asymmetric unilateral tooth flank damage. The hybrid electric vehicle (HEV) mode contributes approximately 88% of total damage to the sun gear’s right flank, dominating system fatigue damage. Transient mode-switching conditions account for approximately 60% of total damage to the sun gear’s left flank, serving as the core damage source. Compared with the traditional full-condition merging method, the proposed mode-decomposed method improves the conservatism of life prediction. This work provides methodological support for refined strength design and targeted optimization of power-split hybrid transmission systems.

World Electric Vehicle Journal doi: 10.3390/wevj17040197

Authors: K. Dhananjay Rao Kapu Venkata Sri Ram Prasad Paidi Pavani Subhojit Dawn Taha Selim Ustun

The increasing need for energy-efficient and environmentally friendly electricity generation has led to the extensive use of hybrid electric systems. These systems integrate different energy sources in an effort to take advantage of the positives of each technology, as using a single source of energy comes with many limitations and disadvantages; hence, the popularity of hybrids has increased in recent times. In this regard, this paper proposes a lithium-ion battery (LIB) and ultracapacitor (UC)-based hybrid architecture considering an optimal energy management framework. In the transportation sector, hybrid vehicles (LIB and UC-based vehicles) effectively utilize the high energy density and power density of LIBs and UCs. This LIB and UC-based hybrid architecture provides an efficient power management solution considering the high power density of the LIB for smooth road profiles, and the high power density of the UC is driven during sudden spikes in load demand because the LIB will not function optimally during the sudden spikes due to lower power density. Furthermore, in order to achieve efficient utilization of the proposed hybrid system, an optimal energy management framework is used. In this regard, in this study, a fractional-order proportional–integral–derivative (FOPID) controller has been designed for effective and optimal energy management. Furthermore, the designed FOPID has been optimized using a metaheuristic technique, namely particle swarm optimization (PSO), to enhance LIB and UC-based hybrid electric vehicle energy management performance. Employing dynamic and optimal energy flow control, the FOPID-based system improves energy consumption, extends LIB life, and improves overall system performance and reliability.

World Electric Vehicle Journal doi: 10.3390/wevj17040196

Authors: Chuang Liu Deng-Wei Yang Cheng-Peng Ma Shang-Xian Zhao Yu-Xuan Zhou Fu-Yun Zhao

The thermal safety and longevity of lithium-ion batteries are critically constrained by excessive temperature rise and spatial thermal non-uniformity, particularly during high-rate discharges. Most existing numerical investigations rely on simplified heat generation models that fail to capture the spatiotemporal heterogeneity of electrochemical heat sources, leading to compromised predictive accuracy. To address this deficiency, this study develops a comprehensive three-dimensional electrochemical–thermal coupled framework, integrating the Newman pseudo-two-dimensional (P2D) electrochemical model with conjugate heat transfer and laminar flow dynamics. The predictive robustness of this framework is rigorously validated against experimental data across multiple discharge rates (3 C and 5 C). The validated model is then deployed to evaluate a water-cooled microchannel cold plate designed for prismatic LiMn2O4/graphite cells under a demanding 5 C discharge. A systematic parametric investigation is conducted to quantify the effects of ambient temperature (293–343 K), microchannel number (2–6), and coolant inlet velocity (0.1–0.6 m/s) on the maximum battery temperature (Tmax) and temperature difference (ΔT). Results demonstrate that the proposed system exhibits exceptional environmental robustness: over a 50 K ambient temperature span, Tmax increases by merely 2.0 K, remaining safely below the 323 K industry limit. Densifying the channel count from 2 to 6 further reduces Tmax by 1.55 K and narrows ΔT to 4.25 K, successfully satisfying the strict 5 K temperature uniformity standard. Furthermore, the thermal benefit of elevating inlet velocity exhibits a pronounced diminishing-return trend governed by the asymptotic reduction in bulk coolant temperature rise, dictating a critical trade-off against the quadratically escalating pumping power. Ultimately, these findings provide robust theoretical guidelines for the rational design of safe and energy-efficient battery thermal management systems.

World Electric Vehicle Journal doi: 10.3390/wevj17040195

Authors: Yuan Li Baijun Lai Jing Wang Yan Sun Donghai Hu Hua Ding

The cathode and anode differential pressure control of a proton exchange membrane fuel cell (PEMFC) directly affects its service life and operating efficiency. Existing control methods find it difficult to cope with strong nonlinear perturbations, and fixed differential pressure control is prone to pressure overshoot and threshold exceedance, resulting in unstable pressure regulation. In order to solve the current research problems, a reinforcement learning method based on hybrid experience replay (HP-TD3) is proposed. A CART-based algorithm is first used to classify the states of the test load, and a load-related segmented reward function is designed. In addition, a hindsight experience replay (HER) mechanism is incorporated into the Priority Experience Replay Twin Delayed Deep Deterministic Policy Gradient (PER-TD3) framework to improve sample utilization efficiency and training stability. Finally, the performance of HP-TD3 and its ability to cope with nonlinear disturbances are verified on a fuel cell control unit hardware-in-the-loop (FCU-HIL) platform. In the A test load (frequent switching and high low-load proportion), the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the degradation index of the fuel cell dynamic performance (Δfc) of HP-TD3 are respectively reduced by 17.4%, 20.5%, and 13.3% compared to P-TD3; in the B test load (high-load operation and low switching frequency), these indicators are reduced by 25.7%, 29.4%, and 15.4% respectively.

World Electric Vehicle Journal doi: 10.3390/wevj17040194

Authors: Ke Yang Xiang Zhou Chicheng Ma

In order to investigate the application potential of lightweight agricultural robots utilizing carbon fiber-reinforced polymer (CFRP) as the primary structural material, this study developed a dedicated rubber-tracked chassis tailored for peanut pest and disease monitoring robots. The chassis design is anchored to the widely applied “single ridge with double rows” cultivation pattern in peanut production and incorporates a real-time track tension monitoring mechanism integrated with pressure sensors. The overall structural configuration of the chassis fully conforms to the standard ridge parameters of mechanized peanut planting while fully considering the intrinsic material properties of CFRP. Additionally, a sprocketless drive wheel structure is specifically adopted to realize higher-precision motion control performance. A mathematical model was constructed to quantitatively characterize the tension correlation between the tight side and slack side of the rubber track, as well as the variation law of initial tension influenced by multiple factors including the total mass of the robot platform. With the curb weight of the robot platform set at 45 kg, the theoretical initial tension is calculated to be 24.5 N (equivalent to approximately 2.5 kg, taking the gravitational acceleration g = 9.8 m/s2). The prototype shows potential for maintaining consistent tension, though a mechanical weakness was identified and will be addressed in future work. Performance validation tests show that the chassis maintains stable operation with no sprocket slippage during field visual inspection.

World Electric Vehicle Journal doi: 10.3390/wevj17040193

Authors: Nalin Kant Mohanty Gandhiram Harishram V. Hareis S. Kumar Vellaiswamy Rajeswari

This article introduces a new Multi-Source DC-DC converter-based smart energy management system on a common DC bus architecture, utilizing solar PV and wind sources for electric vehicle applications. The common DC bus enables coordinated power flow control among multiple sources while maintaining modularity and flexibility. To promote efficient battery charging and discharging, as well as enhanced protection from faults, an artificial neural network (ANN) approach has been incorporated. The main function of the ANN controller is to detect faults in the EV battery for timely intervention. Compared to existing topologies, its coordinated integration and control can operate effectively under dynamic conditions and improve stability. Additionally, the article presents the operating principle, modes of operation, design analysis, and control strategy. The simulation results of the proposed system are evaluated through MATLAB Simulink software 2024b. Furthermore, a 200 W laboratory prototype was developed to validate the system’s dynamic performance under various operating conditions.

World Electric Vehicle Journal doi: 10.3390/wevj17040192

Authors: Ice Poonphol Padej Pao-la-or

Axial Flux Permanent Magnet Synchronous Motors (AFPMSMs) are gaining increasing attention for their application in electric vehicle (EV) drive systems. Their high torque density and compact axial geometry make them attractive for high-performance EV drive systems. However, cogging torque remains a major challenge, degrading low-speed drivability, noise performance, and control stability. This article proposes a magnet skew on rotor modulation structure using a genetic algorithm (GA) to reduce cogging torque in AFPMSMs utilizing a 12/15 non-integer pole/slot arrangement. The objective of optimization is to simultaneously reduce cogging torque under identical electromagnetic constraints. A complete three-dimensional finite element model (3D-FEM) incorporating nonlinear magnetic material properties has been developed to evaluate the electromagnetic field distribution and torque components. The results indicate that a 12/15 non-integer pole/slot arrangement improves harmonic distribution and extends the operating range with lower cogging torque compared to integer pole/slot designs. Combined with GA-optimized skew angles, this reduces peak-to-peak cogging torque to less than 50%. This design is ideally suited for the traction requirements of electric vehicles, including premium electric vehicles where smooth operation at low speeds is critical.