World Electr. Veh. J., Volume 16, Issue 4 (April 2025) – 54 articles

In order to solve the problem of emergency collision avoidance of autonomous vehicles when the front vehicle is unstable and sliding under high-speed conditions, a research method for the state recognition of the front side-skid vehicle and the trajectory prediction of the front side-skid vehicle was proposed. By extracting the key features of the vehicle in front of the vehicle in danger of sliding to build a skidding recognition model of the vehicle in front, a skidding recognition strategy of the vehicle in front was designed based on the extracted skidding feature indexes to judge the skidding state of the vehicle in front. The state quantity of the sliding vehicle in front is selected, and the constant rotation rate and acceleration model (CTRA) is established to predict the trajectory of the sliding vehicle in front in a short time. Considering the simplified assumptions of the model and the noise in the process of sensor perception information, the Unscented Kalman Filter (UKF) is used to deal with the uncertainty in the trajectory prediction process, the possible position and covariance of the front sideslipping vehicle are calculated, and the possible future area of the front sideslipping vehicle is estimated under the condition of a probability of 0.9. Through the established Carsim and Simulink co-simulation platform, the effectiveness of the front vehicle skidding state recognition strategy and the accuracy of the trajectory prediction of the sliding vehicle are verified under the condition of high speed and low attachment. Full article

Reducing carbon dioxide emissions in transportation has become a priority for achieving emission targets. Transitioning to electric vehicles significantly decreases global CO₂ emissions and reduces urban noise and air pollution. The selection of efficient charging strategies for electric bus fleets substantially influences their environmental impact. This study analyzes the charging strategy for electric bus fleets based on real operational data from Győr, Hungary. It evaluates the impact of different charging times and strategies on CO₂ emissions, considering the energy mixes of Hungary, Poland, Germany, and Sweden. A methodology has been developed for defining sustainable and environmentally friendly charging strategies by incorporating operational conditions as well as daily, monthly, and seasonal fluctuations in emission factors. Results indicate substantial potential for emission reduction through the recommended alternative charging strategies, although further studies regarding battery lifespan and economic feasibility of infrastructure investments are recommended. The novelty of this work lies in integrating real charging data with hourly country-specific emission intensity values to assess environmental impacts dynamically. A comparative framework of four charging strategies provides quantifiable insights into emission reduction potential under diverse national energy mixes. Full article

When the alternating current (AC) chassis dynamometer system measures the motion parameters of a test vehicle, it is subject to interference from measurement noise, leading to an increase in testing errors. An innovative adaptive Kalman Filtering (KF) algorithm based on innovation covariance is proposed. This algorithm facilitates the optimal estimation of vehicle motion parameters without necessitating prior error statistics. The loading model of the measurement and control system is optimized, enabling the precise loading of the dynamometer. The test results indicate that the testing error of the optimized algorithm for the loading model decreases from 6.4% to 1.8%. This improvement establishes a foundation for achieving accurate control of the chassis dynamometer and minimizing testing errors. Full article

In this paper, a fault diagnosis method for permanent magnet synchronous motors is proposed, combining wavelet packet transform (WPT) energy feature extraction and a genetic algorithm (GA)-optimized back propagation (BP) neural network. Firstly, for the common types of motor faults (turn-to-turn short-circuit, phase-to-phase short-circuit, loss of magnetism, inverter open-circuit, rotor eccentricity), a corresponding motor fault model is established. The stator current signals during motor operation are analyzed using wavelet packet transform, and energy features are extracted from them as feature vectors for fault diagnosis. Then, a BP neural network is constructed, and a genetic algorithm is used to optimize its initial weights and thresholds, thereby improving the network’s classification accuracy. The results show that the GA-BP model outperforms the SSA-PNN diagnostic model in terms of fault classification accuracy. In particular, for the diagnosis of normal operation, inverter open-circuit, and demagnetization faults, the accuracy rate reaches 100%. This method demonstrates high diagnostic accuracy and practical application value. Full article

The growing demand for sustainable and self-powered technologies in automotive applications has led to increased interest in energy harvesting from vehicle suspensions. Recovering mechanical energy from road-induced vibrations offers a viable solution for powering wireless sensors and autonomous electronic systems, reducing dependence on external power sources. This study presents the design, modeling, and experimental validation of a hybrid energy-harvesting system that integrates piezoelectric and magnetoelectric effects to efficiently convert mechanical vibrations into electrical energy. A model-based systems engineering (MBSE) approach was used to optimize the system architecture, ensuring high energy conversion efficiency, durability, and seamless integration into suspension systems. The theoretical modeling of both piezoelectric and magnetoelectric energy harvesting mechanisms was developed, providing analytical expressions for the harvested power as a function of system parameters. The designed system was then fabricated and tested under controlled mechanical excitations to validate the theoretical models. Experimental results demonstrate that the hybrid system achieves a maximum power output of 16 µW/cm² from the piezoelectric effect and 3.5 µW/cm² from the magnetoelectric effect. The strong correlation between theoretical predictions and experimental measurements confirms the feasibility of this hybrid approach for self-powered automotive applications. Full article

Recent advancements in neural networks (NNs) have underscored their potential for deployment in domains that demand computationally intensive operations, including applications on resource-constrained edge devices. This study investigates the integration of a compact neural network, TinyFC, within the Field-Oriented Control (FOC) framework of a Permanent Magnet Synchronous Motor (PMSM). While proportional–integral (PI) controllers remain a widely adopted choice for FOC due to their simplicity, their performance can degrade significantly under high-frequency speed transitions, where nonlinear dynamics introduce notable inaccuracies. The TinyFC model complements the PI controller by learning the intrinsic dependencies within the control loops and generating corrective signals to alleviate these inaccuracies. To ensure practical implementation, TinyFC underwent extensive optimization procedures, incorporating advanced techniques such as hyperparameter tuning, pruning, and 8-bit quantization. These measures successfully reduced the model’s computational overhead while preserving predictive accuracy. Simulation results demonstrated that embedding TinyFC within the FOC framework substantially reduced overshoot, with the pruned TinyFC entirely eliminating overshoot when integrated into the speed control unit. These findings highlight the feasibility of employing lightweight neural networks for real-time motor control applications, establishing a foundation for more efficient and precise control strategies in edge automotive and industrial systems. Full article

Achieving a stable color output across wide temperature ranges in automotive LED applications is challenging, especially when using cost-sensitive chips with limited computational resources. This study proposes an improved temperature model that integrates Fourier heat conduction and thermal resistance concepts to more accurately capture self-heating and power dissipation effects. To accommodate the constraints of low-cost automotive-grade microcontrollers (MCUs), the associated optical algorithm is converted from floating-point to a 16.16 fixed-point format, reducing both memory usage and computational overhead. Experimental results conducted from −40 °C to 120 °C show that the improved model predicts LED temperatures within 5 °C of measured values, reducing errors by up to 30% compared to conventional PN-junction-based methods. Furthermore, by comparing the chromaticity points generated under the new and traditional models—and implementing an additional three-duty-cycle offset at 1% brightness—the improved approach reduces chromaticity drift by approximately 0.0052 in the CIE 1931 xy color space. These findings confirm the superior stability and accuracy of the new model for both thermal management and chromaticity compensation, offering a cost-effective solution for automotive LED systems requiring precise color control under constrained MCU resources. Full article

An efficient Battery Management System (BMS) specifically for Electric Vehicles is crucial for improving battery run time performance. A primary function of an effective BMS is accurately determining the State of Charge (SOC) and State of Health (SOH) of lithium-ion batteries in Electric Vehicles (EVs). However, many existing studies have concentrated on examining sensor malfunctions in batteries to avert problems such as overcharging and overheating and are lacking in terms of effective handling of non-linear behaviors. To overcome these limitations, the proposed work introduces a hybrid approach for estimating the state of lithium-ion batteries. It employs an Extended Kalman Filter (EKF) for SOC estimation and modified Multi-Layer Perceptron (MLP) for SOH estimation in batteries. It can handle the non-linear characteristics often exhibited by sensor readings and fault behaviors. The EKF algorithm involves initialization, prediction, and correction phases, allowing for accurate state estimation based on measurements. For SOH estimation, the NASA battery dataset, which includes various battery conditions across different temperatures, is analyzed using a modified MLP regression process. This modified MLP employs a gradient shift bias adjustment technique to minimize error rates by refining the gradients and biases introduced during the training process. It also effectively adjusts the model’s weights for better SOH estimation. The results demonstrate improved accuracy in battery performance, as indicated by lower RMSE, MSE, MAE and R² values. Furthermore, the study highlights the effectiveness of this hybrid method for significant battery management at different temperatures, which emphasizes the potential of this model, with enhanced state estimation for EV applications. Full article

As the world accelerates efforts to combat climate change and transition toward a green, low-carbon economy, the new energy vehicle (NEV) industry has become a key driver of carbon reduction. Its ability to innovate efficiently is critical to long-term sustainable development. This study builds on the innovation value chain theory and introduces an evaluation framework that accounts for undesirable outputs such as energy consumption and pollutant emissions. Using a super-efficiency network SBM–Malmquist model and Tobit regression, we analyze the technological innovation efficiency of 272 A-share listed NEV enterprises in China from 2016 to 2023. Expanding beyond traditional overall assessments, we examine efficiency at different stages of the industry chain and find that: (a) overall technological innovation efficiency has declined, mainly due to weak pure technical efficiency, underscoring the need for better R&D management and resource allocation; (b) efficiency varies across the industry chain, with midstream firms performing better than those upstream and downstream, reflecting differences in technological accumulation and market conditions; (c) R&D tax deductions and market competition significantly boost innovation efficiency by creating pressure-driven incentives, while mismatched labor skills, the “welfare dependence” effect of tax incentives and financing constraints hinder progress. By introducing a two-stage innovation efficiency evaluation framework, this study not only pinpoints where efficiency losses occur along the industry chain but also provides empirical insights to guide targeted policy decisions, offering valuable implications for the sustainable growth of the global NEV industry. Full article

Transient road excitation can cause high-frequency impacts to the vehicle, leading to deterioration of smoothness and operational stability, and seriously impairing system life and performance. To address this problem, the vehicle model and the road model containing transient road excitation is first established, and the impact mechanism of transient excitation is simulated and analyzed, from which the fuzzy control of vehicle speed and the model predictive control of the suspension system are designed respectively. The suspension control method based on the speed control is further proposed, which sets the strategy to use fuzzy control to regulate the vehicle speed after the on-board sensors identify the bumpy road excitation, and at the same time to implement the model predictive control for the active suspension system, and dynamically adjusts the control weight parameters to be compatible with the vehicle speed control. The simulation results show that compared with the single suspension control, the control strategy improves in all stages, and the root mean square values of body acceleration, pitch angle acceleration, and front and rear tire dynamic loads are reduced by 9.52%, 4.55%, 29.5% and 17.8%, respectively, and the peaks are reduced by 23.8%, 39.9%, 44.7% and 33.2%, respectively, which further enhances the safety and ride comfort of the vehicle. Finally, the effectiveness and correctness of the strategy are verified by a hardware-in-the-loop test. Full article

The increasing computational complexity of Model Predictive Control (MPC) in battery systems limits its practical adoption, despite its potential for optimizing performance under dynamic operating conditions. To address this challenge, this study introduces an Artificial Neural Network-based MPC framework (MPCANN) tailored for VTC6 3Ah lithium-ion cells, aiming to reduce computational burdens while retaining predictive accuracy. The framework synergizes MPC’s predictive capabilities with the daptive learning of Artificial Neural Network (ANN) by training the ANN offline using MPC-derived input–output data. Validation against prior MPC results demonstrates MPCANN’s ability to replicate MPC behavior across temperatures, achieving strong alignment in current and temperature predictions. While state of charge (SoC) estimation accuracy requires refinement at elevated temperatures, the framework reduces computation time by 94% compared to traditional MPC, highlighting its efficiency. These results underscore MPCANN’s potential to enable real-time implementation of advanced battery control strategies, offering a pathway to balance computational efficiency with performance in adaptive energy systems. Full article

To effectively address the challenges of imbalanced equipment utilization, frequent congestion, and poor economic benefits faced by charging and swapping stations (ICSSs), this paper innovatively proposes a comprehensive scheduling strategy that combines user behavior regulation and battery management. In terms of user regulation, an intention-reshaping model for changing user cognition is proposed to equalize the use of charging and swapping (CAS) equipment, easing ICSS congestion. Moreover, an off-station scheduling model for electric vehicles (EVs) is developed to enhance overall ICSS revenue. Within the battery management terms, the suggested inventory battery threshold adjustment method and charging strategy by charging time segmentation are employed to ensure consistent inventory battery supply and cost-effective battery charging. Finally, a two-stage scheduling strategy of in-station and off-station scheduling is suggested for the ICSS, and an improved northern goshawk optimization algorithm (INGO) is used to solve it. The results showed that this strategy reduced the overall congestion of ICSSs by 34% and increased the average annual net revenue by 64%. The goal of alleviating congestion and improving the economic efficiency of ICSSs has been achieved. Full article

This research investigates the impact of cultural dimensions on service quality expectations in electric vehicle (EV) dealerships. Grounded in Hofstede’s cultural theory and employing a data-driven approach, the study utilizes a series of robust analytical techniques, including the SVM algorithm, factor analysis, and ANOVA. Through a comprehensive analysis of EV customers’ expectations for expertise, empathy, and responsiveness, the findings reveal that cultural dimensions significantly shape service quality expectations, regardless of the service provider’s gender. Notably, consumers with a stronger masculine orientation have lower expectations for expertise but higher expectations for empathy than those with a stronger feminine orientation. These findings challenge the traditional emphasis on gender as a key factor in service quality expectations and underscore the need to incorporate cultural values in service strategy design and quality improvement in the EV industry. Full article

The suspension system in plays a pivotal role, especially in off-road vehicles, in ensuring optimal comfort, road holding and ride safety. This study explores the transition from a MacPherson strut to a double wishbone suspension system, emphasizing its impact on relevant suspension features, such as camber and caster angles, motion ratio and vertical dynamics. Through this study, an off-road vehicle was retrofitted with the proposed suspension architecture and tested both numerically and experimentally. Test results reproduce simulation outcomes, thus confirming the effectiveness of the redesigned suspension system for the target vehicle, especially for demanding off-road applications. Full article

Green hydrogen is increasingly recognized as a sustainable energy vector, offering significant potential for the industrial sector, buildings, and sustainable transport. As countries work to establish infrastructure for hydrogen production, transport, and energy storage, they face several challenges, including high costs, infrastructure complexity, security concerns, maintenance requirements, and the need for public acceptance. To explore these challenges and their environmental impact, this study proposes a hybrid sustainable infrastructure that integrates photovoltaic solar energy for the production and storage of green hydrogen, with PEMFC fuel cells and a hybrid Power-to-Electricity (PtE) and Power-to-Gas (PtG) configurations. The proposed system architecture is governed by an innovative energy optimization and management (EMS) algorithm, allowing forecasting, control, and supervision of various PV–hydrogen–Grid transfer scenarios. Additionally, comprehensive daily and seasonal simulations were performed to evaluate power sharing, energy transfer, hydrogen production, and storage capabilities. Dynamic performance assessments were conducted under different conditions of solar radiation, temperature, and load, demonstrating the system’s adaptability. The results indicate an overall efficiency of 62%, with greenhouse gas emissions reduced to 1% and a daily production of hydrogen of around 250 kg equivalent to 8350 KWh/day. Full article

The integration of electric vehicles into modern mobility ecosystems relies heavily on reliable charging station infrastructures that support real-time communications and data-driven functionalities. Existing solutions often face security vulnerabilities in their firmware update mechanisms, compromising safety, user trust, and the broader deployment of these stations in emerging digital and connected environments. This paper aims to address these gaps by proposing a blockchain-based framework designed to provide secure, tamper-proof firmware updates for charging stations in a Web of Things environment. The approach uses decentralized ledger technologies to validate firmware integrity, authenticate update sources, and mitigate the risk of malicious or fraudulent content. In a comprehensive experimental setup, the proposed method demonstrates enhanced resilience against unauthorized firmware modifications and improved traceability of update transactions through immutable records. Results highlight a reduction in firmware compromise events, as well as improved detection and notification efficiencies in real-time networked systems. These findings suggest that integrating blockchain technology into firmware update workflows strengthens security in electric vehicle charging infrastructures. Consequently, the adoption of decentralized verification approaches can drive broader trust in connected mobility services, supporting safer and more efficient charging station networks while fostering future innovation in sustainable transport. Full article

This study presents the first investigation into the problem of autonomous vehicle (AV) merging into existing platoons, proposing a multi-agent deep reinforcement learning (MA-DRL)-based cooperative control framework. The developed MA-DRL architecture enables coordinated learning among multiple autonomous agents to address the multi-objective coordination challenge through synchronized control of platoon longitudinal acceleration, AV steering and acceleration. To enhance training efficiency, we develop a dual-layer multi-agent maximum Q-value proximal policy optimization (MAMQPPO) method, which extends the multi-agent PPO algorithm (a policy gradient method ensuring stable policy updates) by incorporating maximum Q-value action selection for platoon gap control and discrete command generation. This method simplifies the training process by using maximum Q-value action policy optimization to learn platoon gap selection and discrete action commands. Furthermore, a partially decoupled reward function (PD-Reward) is designed to properly guide the behavioral actions of both AVs and platoons while accelerating network convergence. Comprehensive highway simulation experiments show the proposed method reduces merging time by 37.69% (12.4 s vs. 19.9 s) and energy consumption by 58% (3.56 kWh vs. 8.47 kWh) compared to existing methods (the quintic polynomial-based + PID (Proportional–Integral–Differential)). Full article

An accurate state of health (SOH) estimation for lithium-ion batteries (LIBs) is crucial for reliable operations and extending service life. While electrochemical impedance spectroscopy (EIS) effectively characterizes LIBs degradation patterns, the high dimensionality of EIS data poses challenges for an efficient analysis. This study proposes a novel method that combines EIS with an equivalent circuit model (ECM) and distribution of relaxation time (DRT) analysis to extract low-dimensional health features from high-dimensional EIS data. A multi-scale kernel extreme learning machine (MS-KELM), optimized by the Sparrow Search Algorithm (SSA), estimates battery SOH with an average mean absolute error (MAE) of 1.37% and a root mean square error (RMSE) of 1.76%. In addition, compared with support vector regression (SVR) and Gaussian process regression (GPR), the proposed method reduces computational time by factors of 4 to 30 and lowers memory usage by approximately 18%. Full article

Since permanent magnets and rotors are core components of electric vehicle drive motors, accurate diagnosis of demagnetization and eccentricity faults is crucial for ensuring the safe operation of electric vehicles. Currently, intelligent diagnostic methods based on three-phase current signals have been widely adopted due to their advantages of easy acquisition, low cost, and non-invasiveness. However, in practical applications, the fault characteristics in current signals are relatively weak, leading to diagnostic performance that falls short of expected standards. To address this issue and improve diagnostic accuracy, this paper proposes a novel diagnostic method. First, branch current is utilized as the data source for diagnosis to enhance the fault characteristics of the diagnostic signal. Next, a dual-modal feature extraction module is constructed, employing Variational Mode Decomposition (VMD) and Fast Fourier Transform (FFT) to concatenate the input branch current along the feature dimension in both the time and frequency domains, achieving nonlinear coupling of time–frequency features. Finally, to further improve diagnostic accuracy, a cascaded convolutional neural network based on dilated convolutional layers and multi-scale convolutional layers is designed as the diagnostic model. Experimental results show that the method proposed in this paper achieves a diagnostic accuracy of 98.6%, with a misjudgment rate of only about 2% and no overlapping feature results. Compared with existing methods, the method proposed in this paper can extract higher-quality fault features, has better diagnostic accuracy, a lower misjudgment rate, and more excellent feature separation ability, demonstrating great potential in intelligent fault diagnosis and maintenance of electric vehicles. Full article

Lithium-ion batteries are essential for electric vehicles, requiring critical resources such as lithium and cobalt. Paraguay’s integration into the electric vehicle supply chain presents an opportunity to leverage its renewable energy and strategic location. This study evaluates potential partners for Paraguay to establish a lithium-ion battery and electric vehicle assembly plant in the Chaco region. A multi-criteria decision analysis using the Analytic Hierarchy Process and expert opinions assessed Argentina, Brazil, Bolivia, and Chile based on economic, energy, environmental, social, political, and geopolitical factors. The results indicate Chile as the most favorable partner (29.5%), followed by Argentina (25.9%), Bolivia (22.8%), and Brazil (21.6%). Chile’s strengths lie in its environmental policies and political stability, while Argentina offers logistical advantages and resource availability. The findings highlight strategic pathways for Paraguay’s integration into the electric vehicle supply chain and the importance of targeted collaboration to enhance regional lithium-ion battery production and commercialization. Full article

The rapid adoption of electric vehicles (EVs) and the increasing reliance on renewable energy sources necessitate innovative charging infrastructure solutions to address key challenges in energy efficiency, grid stability, and sustainable transportation. Dynamic wireless charging systems, which enable EVs to charge while in motion, offer a transformative approach to mitigating range anxiety and optimizing energy utilization. However, these systems face significant operational challenges, including dynamic traffic conditions, uncertain EV arrival patterns, energy transfer efficiency variations, and renewable energy intermittency. This paper proposes a novel quantum computing-assisted optimization framework for the modeling, operation, and control of wireless dynamic charging infrastructure in urban highway networks. Specifically, we leverage Variational Quantum Algorithms (VQAs) to address the high-dimensional, multi-objective optimization problem associated with real-time energy dispatch, charging pad utilization, and traffic flow coordination. The mathematical modeling framework captures critical aspects of the system, including power balance constraints, state-of-charge (SOC) dynamics, stochastic vehicle arrivals, and charging efficiency degradation due to vehicle misalignment and speed variations. The proposed methodology integrates quantum-inspired optimization techniques with classical distributionally robust optimization (DRO) principles, ensuring adaptability to system uncertainties while maintaining computational efficiency. A comprehensive case study is conducted on a 50 km urban highway network equipped with 20 charging pad segments, supporting an average traffic flow of 10,000 EVs per day. The results demonstrate that the proposed quantum-assisted approach significantly enhances energy efficiency, reducing energy losses by up to 18% compared to classical optimization methods. Moreover, traffic-aware adaptive charging strategies improve SOC recovery by 25% during peak congestion periods while ensuring equitable energy allocation among different vehicle types. The framework also facilitates a 30% increase in renewable energy utilization, aligning energy dispatch with periods of high solar and wind generation. Key insights from the case study highlight the critical impact of vehicle alignment, speed variations, and congestion on wireless charging performance, emphasizing the need for intelligent scheduling and real-time optimization. The findings contribute to advancing the integration of quantum computing into sustainable transportation planning, offering a scalable and robust solution for next-generation EV charging infrastructure. Full article

In the emerging paradigm of embodied intelligence, eVTOL technology holds significant potential to transform the low-altitude economy, particularly in short-distance emergency logistics and urban distribution. Companies like Meituan and Shunfeng (SF) are pioneering fixed low-altitude routes to reduce reliance on human delivery. We first investigate the performance and routing of Meituan’s eVTOL system, focusing on the dynamic optimization of eVTOL reserves and total costs at distribution stations under fluctuating order surges and charging constraints. An iterative algorithm is constructed, supported by numerical examples and Monte Carlo simulations. Our results reveal that cost parameters and demand characteristics jointly shape eVTOL incremental decision-making and its economic performance. To optimize costs, strategies like multi-period decentralized scheduling or low-frequency centralized decision-making are proposed. Future research will address limitations such as 2C charging effects and joint battery-eVTOL replenishment to further advance urban logistics and low-altitude economy development. Full article

This study examines the smoothness of an intelligent chassis for electric vehicles, analyzes the chassis structure and configuration, and considers the impacts of the primary energy subsystem, electric drive subsystem, and auxiliary control subsystem on smoothness. The influence of suspension parameters on smoothness is examined, highlighting the significance of elastic element stiffness and the shock absorber damping ratio. Dynamic models of quarter- and half-car suspension systems, as well as a comprehensive nine-degree-of-freedom vehicle model, are developed to examine the vibration characteristics under varying road conditions. The chassis suspension dynamic model is developed, simulated, and analyzed using ADAMS/View software 2024. The suspension damping value is optimized with the ADAMS/PostProcessor tool, revealing that smoothness can be enhanced by judiciously decreasing the damping value. The article discusses the human body’s reaction to vibration and assessment metrics, referencing worldwide standards to establish a foundation for evaluation. The study offers theoretical backing for the design and optimization of an intelligent chassis, hence advancing the technological development of electric vehicles. Full article

With the rapid development of the electric vehicle (EV) industry, the importance of battery management systems (BMS) in ensuring the safety, reliability, and efficiency of batteries has significantly increased. This study explores the technological development trends and market layout of EV BMS through patent analysis, focusing on patent quantity, geographic distribution, and technical classification. By integrating the analytic hierarchy process (AHP) and entropy weight method, a patent value evaluation model was constructed to identify key patents and assess their quality across four dimensions: technical, market, economic, and legal. The results reveal that BMS patents are primarily concentrated in China, the United States, and South Korea, with major contributors including LG Energy Solution, BYD, and Hyundai. While BMS patent applications grew rapidly from 2015 to 2020, the pace has slowed since 2021, indicating a possible shift in market focus. The analysis identified 14 high-quality patents, mainly focused on battery safety and compactness, while fewer patents addressed battery lifespan extension and anti-interference capabilities. The study suggests that although significant progress has been made in BMS technology, there is still substantial room for innovation, particularly in areas such as battery lifespan management, charging efficiency, and intelligent energy scheduling. This research provides valuable insights for future technological innovation and market decision-making in the EV BMS sector. Full article

Open-pit mining involves the use of vehicles with high load capacity and satisfactory mobility. As experience shows, these requirements are fully met by pneumatic wheeled dump trucks, the traction drives of which can be made using thermal or electric machines. The latter are preferable due to their environmental friendliness. Unlike dump trucks with thermal engines, which require fuel to be injected into them, electric trucks can be powered by various options of a power supply: centralized, autonomous, and combined. This paper highlights the advantages and disadvantages of different power supply systems depending on their schematic solutions and the quarry parameters for all the variants of the power supply of the dumper. Each quantitative indicator of each factor was changed under conditions consistent with the others. The steepness of the road elevation in the quarry and its length were the factors under study. The studies conducted show that the energy consumption for dump truck movement for all variants of a power supply practically does not change. Another group of factors consisted of electric energy sources, which were accumulator batteries and double electric layer capacitors. The analysis of energy efficiency and the regenerative braking system reveals low efficiency of regeneration when lifting the load from the quarry. In the process of lifting from the lower horizons of the quarry to the dump and back, kinetic energy is converted into heat, reducing the efficiency of regeneration considering the technological cycle of works. Taking these circumstances into account, removing the regenerative braking systems of open-pit electric dump trucks hauling soil or solid minerals from an open pit upwards seems to be economically feasible. Eliminating the regenerative braking system will simplify the design, reduce the cost of a dump truck, and free up usable volume effectively utilized to increase the capacity of the battery packs, allowing for longer run times without recharging and improving overall system efficiency. The problem of considering the length of the path for energy consumption per given gradient of the motion profile was solved. Full article

This study examines the efficacy of a seat inertial suspension system in relation to vibration isolation and energy recovery in electric commercial vehicles. The research focuses on the structural modifications of the suspension system that arise from the incorporation of an inerter, a novel vibration isolation component. A dynamic model of the seat inertial suspension is constructed, which includes two different structures consisting of components connected in parallel and in series. The analysis explores how the absorption of suspension parameters affects both seat comfort and the characteristics of energy harvesting. Furthermore, an optimal design methodology for the seat inertial suspension is proposed, seat comfort and energy recovery efficiency are also taken into consideration. The findings reveal that the parallel-structured seat inertial suspension system demonstrates superior overall performance. Specifically, it achieves a 36.6% reduction in seat acceleration, a 55.3% decrease in suspension working space, and an energy harvesting efficiency of 41.9%. The seat inertial suspension significantly improves occupant comfort by reducing seat acceleration, significantly reducing the amplitude of seat suspension movement, and recovering most of the seat suspension’s vibration energy, in comparison to traditional seat suspension systems. Full article

As transportation systems evolve, ensuring safe and efficient mobility in Intelligent Transportation Systems remains a priority. Work zones, in particular, pose significant safety challenges due to lane closures, which can lead to abrupt braking and sudden lane changes. Most previous research on Connected and Autonomous Vehicles (CAVs) assumes ideal communication conditions, overlooking the effects of message loss and network unreliability. This study presents a comprehensive smart work zone (SWZ) framework that enhances lane-change safety by the integration of both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. Sensor-equipped SWZ barrels and Roadside Units (RSUs) collect and transmit real-time hazard alerts to approaching CAVs, ensuring coverage of critical roadway segments. In this study, a co-simulation framework combining VEINS, OMNeT++, and SUMO is implemented to assess lane-change safety and communication performance under realistic network conditions. Findings indicate that higher Market Penetration Rates (MPRs) of CAVs can lead to improved lane-change safety, with time-to-collision (TTC) values shifting toward safer time ranges. While lower transmission thresholds allow more frequent communication, they contribute to earlier network congestion, whereas higher thresholds maintain efficiency despite increased packet loss at high MPRs. These insights highlight the importance of incorporating realistic communication models when evaluating traffic safety in connected vehicle environments. Full article

In multi-load wireless power transfer (WPT) systems, when multiple loads simultaneously charge using the same transmitter, the unpredictable spatial positions of the loads and the presence of cross-coupling make it challenging to achieve complete system decoupling, thereby limiting the effective power reception area. To address this issue, this paper investigates a one-to-multiple WPT system based on a single-transistor P^#-type LCC-S compensation network. Air-core coils are employed at the receiving end to mitigate cross-coupling, and the effective power reception area is analyzed. First, the operating principle of the system is examined and the parameter configuration conditions for the resonant circuit are derived. Then, MATLAB/Simulink R2022b is used to establish simulation circuit models for both single-transmitter single-receiver and single-transmitter dual-receiver WPT systems. The results indicate that for an effective output power of 5 W, the mutual inductance ranges are (3.5, 6) μH and (3, 6.5) μH, respectively. Next, finite element simulations are conducted to analyze the mutual inductance variations caused by spatial misalignment of the coils. For the single-transmitter single-receiver system, when the transmission distance is 5–12.5 mm, the effective power reception area corresponds to an X- and Y-axis misalignment of ±15 mm, while at a transmission distance of 10 mm, the effective reception area is ±10 mm along both axes. In the single-transmitter dual-receiver system, for a transmission distance of 5–14 mm, the maximum reception area is ±15 mm along the X-axis and ±10 mm along the Y-axis. Finally, an experimental platform is built to verify that multiple loads at different positions can achieve effective power reception for charging. Full article

This research investigates the core competency indicators required for battery electric vehicle (BEV) sales personnel to effectively contribute to the growth of the BEV industry and the transition toward sustainable mobility. As global efforts to reduce carbon emissions intensify, this study identifies the necessary competencies to equip BEV sales teams in navigating the complexities of BEV adoption. This study employs a structured Delphi methodology, gathering insights from a panel of 15 industry professionals, to define and validate key competency dimensions. These competencies are categorized into four main dimensions—professional knowledge, professional ability, professional attitude, and personal traits—and further subdivided into 20 sub-dimensions and 58 specific indicators. Essential competencies include technical expertise in BEV technology, communication skills, customer relationship management, sales techniques, and proficiency in after-sales services. The findings emphasize the significant role of continuous learning, work attitude, and the integration of digital tools in driving sales effectiveness and customer trust. Furthermore, the competency framework developed in this study aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 9 (industry, innovation, and infrastructure), SDG 11 (sustainable cities and communities), and SDG 4 (quality education). The framework offers practical insights for recruitment, training, and performance evaluation, ensuring that BEV sales personnel are well-prepared to foster the widespread adoption of electric vehicles, thereby contributing to a sustainable and low-carbon future. Full article

Hybrid propulsion systems have become a focal point of low-carbon aviation research due to their advantages in energy savings, emissions reduction, and noise abatement. This study develops an integrated design methodology for hybrid propulsion systems for aircraft, incorporating multidisciplinary algorithms to establish an overall performance model. Building on this model, a comprehensive aircraft design platform was constructed, and its simulation capabilities were validated. Focusing on the mission requirements of a 180-seat narrow-body airliner, this study analyzed and compared the characteristics of three hybrid propulsion architectures, optimized their design schemes, and evaluated the key technologies for each architecture. A sensitivity analysis was conducted for critical technologies within the turboelectric architecture. The results indicate that, based on current data and future projections, a turboelectric system featuring batteries with a specific energy of 500 Wh/kg and installed motor power of 3 MW demonstrates superior performance, reduced fuel consumption, and no additional energy storage burden, making it the preferred propulsion solution. Furthermore, enhancing the utilization of aft-mounted fans and increasing the power blending coefficient can improve system performance. However, the maximum power blending coefficient is constrained to 27.25% by the specific motor power capacity. Full article