World Electr. Veh. J., Volume 16, Issue 1 (January 2025) – 54 articles

Countries worldwide are increasingly focused on addressing the imbalance between the supply and demand for EV charging infrastructure, with the community-shared charging post (CSCP) co-construction project emerging as a promising solution. The broad participation and investment support of the residents are the keys to the success of the CSCP co-construction project. This study, grounded in the theory of planned behavior (TPB) from social psychology, incorporated factors such as community identity, perceived green value, economic benefit, uncivil behaviors, and perceived risk to construct a structural model explaining community residents’ intention to invest in the CSCP co-construction project. This research confirmed that (1) 85.73% of respondents expressed strong recognition of the CSCP co-construction project, with a mean recognition score of 5.56 out of a possible 7; (2) an individual’s social-related perceptions, including the subjective norms and community identity are the strongest determinant of the intention to invest in the CSCP co-construction project; (3) the willingness to invest in CSCP co-construction project differs significantly between the EV group and the non-EV group. Economic benefit was significant only for the non-EV group, while uncivil behaviors were significant only for the EV group. These results provide valuable guidelines for governments and corporations that are promoting or pursuing sharing community for the residents. Full article

Given the rapid development of technologies such as new energy vehicles, autonomous driving, and vehicle-to-everything (V2X) communication, a mixed traffic flow comprising connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) is anticipated to emerge. This necessitates the development of a daily dynamic evolution model for mixed traffic flow to address the dynamic traffic management needs of urban environments characterized by mixed traffic. The daily dynamic evolution model can capture the temporal evolution of traffic flow in road networks, with a focus on the daily path choice behavior of travelers and the evolving traffic flow in the network. First, based on the travel characteristics of CAVs and HDVs, the user group in a connected autonomous driving environment is classified into three categories, each adhering to the system optimal (SO) criterion, the user equilibrium (UE) criterion, or the stochastic user equilibrium (SUE) criterion. Next, the pure HDV traffic capacity BPR (Bureau of Public Roads) function is adapted into a heterogeneous traffic flow travel time function to compute the travel time cost for mixed traffic flow. Based on the energy consumption calculation formula for HDVs, the impact of CAVs is fully considered to establish the travel energy consumption cost for both CAVs and HDVs. The total individual travel cost for CAVs and HDVs encompasses both travel time cost and energy consumption cost. Furthermore, a daily dynamic evolution model for mixed traffic flow in a connected autonomous driving environment is developed using the proportional-switch adjustment process (PAP) model. The fundamental properties of the model are validated. Finally, numerical simulations on an N-dimensional (N-D) network confirm the validity and effectiveness of the daily evolution model for mixed traffic flow. A sensitivity analysis of traveler responses in the daily evolution model reveals that, as the sensitivity of CAVs to impedance changes increases, the fluctuations in mixed traffic flow during the early stages of evolution become more pronounced, and the time required to reach a mixed-equilibrium state decreases. Therefore, the PAP-based daily dynamic evolution model for mixed traffic flow effectively captures the evolution process of CAV and HDV mixed traffic flow and supports urban traffic management in a connected autonomous driving environment. Full article

The rapid growth in e-commerce calls for research on the potential of electric vehicles in improving last-mile delivery. Whereas existing studies have examined aspects of last-mile delivery, such as challenges, acceptance/benefits, and feasibility, the studies are fragmented, with conflicting findings and regional differences. Thus, there is a need for a comprehensive understanding of the studies to map out current research trends and propose future research agendas. To address this research gap, a bibliometric review was conducted on 375 publications from the Scopus database. Findings reveal that pioneering countries such as the USA have researched integrating electric vehicles into last-mile delivery systems, focusing on technological advancements such as battery technologies and smart grids. The sustainability theme is common in most studies, focusing on controlling carbon emissions and energy efficiency. The electric micro-mobility theme has grown in recent years, while emerging technologies remain underexplored, especially in developing economies. Future research should address the underexplored areas. These include charging infrastructure optimisation, electric micro-mobility innovations, and integration in urban environments, alongside the social and ethical implications of electric vehicle adoption for last-mile delivery. Full article

With the recent proliferation of electric vehicles, there is increasing attention on drive motors that are powerful and efficient, with a higher power density. To meet such high power density requirements, the cooling technology used for drive motors is particularly important. To further optimize the cooling effects, the use of direct oil-cooling technology for drive motors is gaining more attention, especially regarding the requirements for electric vehicle electric oil pumps (EOPs) in motor cooling. In such high-temperature environments, it is also necessary for the EOP to maintain its performance under high temperatures. This research explores the feasibility of using high-temperature-resistant ferrite magnets in the rotors of EOPs. For a 150 W EOP motor with the same stator size, three different rotor configurations are proposed: a surface permanent magnet (SPM) rotor, an interior permanent magnet (IPM) rotor, and a spoke-type IPM rotor. While the rotor sizes are the same, to maximize the power density while meeting the rotor’s mechanical strength requirements, the different rotor configurations make the most use of ferrite magnets (weighing 58 g, 51.8 g, and 46.3 g, respectively). Finite element analysis (FEA) was used to compare the performance of these models with that of the basic rotor design, considering factors such as the no-load back electromotive force, no-load voltage harmonics (<10%), cogging torque (<0.1 Nm), load torque, motor loss, and efficiency (>80%). Additionally, a comprehensive analysis of the system efficiency and energy loss was conducted based on hypothetical electric vehicle traction motor parameters. Finally, by manufacturing a prototype motor and conducting experiments, the effectiveness and superiority of the finite element method (FEM) design results were confirmed. Full article

Today, transportation plays a crucial role in economic development and establishing strong social relationships. Primary mobility challenges in cities include high levels of traffic, accidents, and pollution. Improvements in road infrastructure, technological advancements at traffic light intersections, and the adoption of electric or hybrid vehicles are insufficient to resolve these issues. Maximizing the use of public transit and shared transportation is essential for this purpose. Strategies aimed at reducing the number of private vehicles on city roads are beneficial in this regard. Ridesharing, particularly carpooling, is an effective strategy to achieve such a reduction in vehicle numbers. However, safety concerns related to carpooling tools present a significant barrier to the growth of this mode of transportation. The measures implemented in these tools often lack appropriate technology for the authentication process, which is crucial for enhancing safety for both passengers and drivers. This proposed research explores the benefits of improving the authentication processes for passengers and drivers within a shared transportation system to minimize information security risks. A thorough literature review was conducted on shared transportation, user registration, authentication processes within these systems, and technologies that could enhance security, such as blockchain. Subsequently, considering the identified criteria in the literature review, a proposal was developed for creating a registration and authentication module based on blockchain that could be applied across various systems. Finally, an analysis was conducted on how this module could be integrated into a carpooling application and the benefits it would provide regarding safety and increased user adoption. The findings from the review were organized and assessed to identify key aspects for improving user authentication in a system based on intelligent transportation systems (ITSs) and utilizing blockchain, recognized for its security and data integrity. The registration and authentication module developed in this work allows increased security, scalability, and user adoption for any type of application, e.g., carpooling. Full article

This study investigates the enhancement of power transfer efficiency (PTE) in wireless power transfer (WPT) systems for electric vehicles (EVs) through simulations and experimental evaluations using metamaterial (MTM) configurations. The MTM model, validated against existing research, was designed for operation at 85 kHz. The influence of MTM on the magnetic field alignment and flux density at the receiver coil significantly improved PTE compared to systems without an MTM configuration. We tested various arrangements of three, six, and nine MTM cells positioned at left, right, top, bottom, and combined locations across coil distances of 0–5.0 cm. The results showed that a nine-cell MTM arrangement yielded greater PTE than a three-cell arrangement because of improved electromagnetic flux distribution. However, the T-shaped arrangement of six MTM cells achieved the maximum PTE at a 2.0 cm coil distance. This performance exceeded that of the configuration with 3 × 3 MTM cells, indicating that the T-shaped design optimizes electromagnetic flux distribution. The six-cell T-shaped arrangement boosted the PTE by 7.7% compared to the nine-cell version, demonstrating its potential as an innovative and efficient WPT system for future EV applications. Full article

With the rapid advancement of technologies related to unmanned ground systems, ground vehicles are being widely deployed across various domains. However, when operating in complex, soft terrain environments, the low bearing capacity of such terrains poses a significant challenge to vehicle mobility. This paper presents a comprehensive review of mobility prediction methods for ground vehicles in off-road environments. We begin by discussing the concept of vehicle mobility, followed by a systematic and thorough summary of the primary prediction methods, including empirical, semi-empirical, numerical simulation, and machine learning approaches. The strengths and weaknesses of these methods are compared and analyzed in detail. Subsequently, we explore the application scenarios of mobility prediction in military operations, subsea work, planetary exploration, and agricultural activities. Finally, we address several existing challenges in current mobility prediction methods and propose exploratory research directions focusing on key technologies and applications, such as real-time mobility prediction, terrain perception, path planning on deformable terrain, and autonomous mobility prediction for unmanned systems. These insights aim to provide valuable reference points for the future development of vehicle mobility prediction methods. Full article

In a sustainable energy system, managing the charging demand of electric vehicles (EVs) becomes increasingly critical. Uncontrolled charging behaviors of large-scale EV fleets will exacerbate loads imbalanced in a multi-microgrid (MMG). At the same time, the time cost of users will increase significantly. To improve users’ charging experience and ensure stable operation of the MMG, we propose a new joint scheduling strategy that considers both time cost of users and spatial load balancing among MMGs. The time cost encompasses many factors, such as traveling time, queue waiting time, and charging time. Meanwhile, spatial load balancing seeks to mitigate the impact of large-scale EV charging on MMG loads, promoting a more equitable distribution of power resources across the MMG system. Compared to the Shortest Distance Matching Strategy (SDMS) and the Time Minimum Matching Strategy (TMMS) methods, our approach improves the average peak-to-valley ratio by 9.5% and 10.2%, respectively. Similarly, compared to the Load Balancing Matching Strategy (LBMS) and the Improved Load Balancing Matching Strategy (ILBMS) methods, our approach reduces the average time cost by 31.8% and 25% while maintaining satisfactory spatial load balancing. These results demonstrate that the proposed method achieves good results in handling electric vehicle scheduling problems. Full article

This study explores electric vehicle (EV) adoption in Jordan, focusing on key transitional factors. It examines government policies, market dynamics, technological advancements, and infrastructure development through semi-structured interviews with key stakeholders, including government officials, industry experts, and consumers. The study provides insights into the economic prospects, infrastructure requirements, and regulatory measures necessary for widespread EV adoption. Government incentives, such as tax exemptions and reduced registration fees, are crucial, but challenges like insufficient charging infrastructure, high initial costs, and limited public awareness persist. Collaborative efforts between the public and private sectors are essential to develop resilient infrastructure, enhance consumer education, and foster technological innovation. The findings underscore the importance of government incentives and coordinated efforts to develop charging infrastructure and raise public awareness. Future research should focus on quantitative methods to validate these findings and explore additional strategies to overcome identified barriers. Full article

Electric vehicles can substantially lower the overall carbon footprint of the transportation sector, and their batteries become key enablers of widespread electrification. Although high capacity and efficiency are essential for providing sufficient range and performance in electric vehicles, they can be compromised by the need to lower costs and environmental impacts and retain valuable materials. In the present work, multi-criteria decision analysis was adopted to assess the sustainability of different lithium-ion batteries. Life cycle carbon emissions and toxicity, material criticality, life cycle costs, specific energy, safety, and durability were considered in the analysis as key parameters of the transition to electric mobility. A subjective approach was chosen for the weight attribution of the criteria. Although certain alternatives, like lithium nickel cobalt manganese oxide (NCM) and lithium nickel cobalt aluminum oxide (NCA), outweigh others in specific energy, they lack in terms of safety, material preservation, and environmental impact. Addressing cost-related challenges is also important for making certain solutions competitive and largely accessible. Overall, while technical parameters are crucial for the development of lithium-ion batteries, it is equally important to consider the environmental burden, resource availability, and economic factors in the design process, alongside social aspects such as the ethical sourcing of materials to ensure their sustainability. Full article

Energy hub stations represent a transformative approach to modern energy systems, functioning as flexible nodes within distribution networks. By seamlessly integrating electric vehicles (EVs) and battery energy storage systems (BESSs), these hubs address critical challenges such as grid stress, renewable energy utilization, and peak load management. This study introduces a novel mathematical optimization model designed to maximize the operational flexibility of EV charging stations by transforming them into fully functional energy hubs. The model incorporates key parameters such as energy demands, load profiles, and grid conditions to optimize the sizing and operation of distributed energy resources, including photovoltaic (PV) systems and BESSs. The proposed approach minimizes grid dependency, enabling energy hubs to efficiently operate varying levels of operational flexibility, from full reliance on grid power to complete independence. The results reveal that, by effectively shifting energy usage away from peak periods and leveraging PV generation, energy hubs emerge as a critical component of sustainable energy systems and can independently support approximately 45% of their load, with minimal PV and BESS capacities. It also reveals a direct correlation between higher flexibility levels and increased infrastructure requirements for PV systems and BESSs. The proposed model underscores the critical role of energy hubs in facilitating the global shift toward decarbonization, aligning with contemporary goals for resilient and environmentally sustainable energy ecosystems. This work provides a scalable framework for future energy systems, with significant implications for policymakers, researchers, and practitioners in the fields of renewable energy and sustainable mobility. Full article

The potential of electric vehicles (EVs) to support the decarbonization of the transportation sector, crucial for meeting greenhouse gas reduction targets under the Paris Agreement, is obvious. Despite their advantages, the adoption of electric vehicles faces limitations, particularly those related to battery range and charging times, which significantly impact the time needed for a trip compared to their combustion engine counterparts. However, recent improvements in fast charging technology have enhanced these aspects, making EVs more suitable for both daily and long-distance trips. EVs can now deal with long trips, with travel times only slightly longer than those of internal combustion engine (ICE) vehicles. Fast charging capabilities and infrastructure, such as 350 kW chargers, are essential for making EV travel times comparable to ICE vehicles, with brief stops every 2–3 h. Additionally, EVs help reduce noise pollution in urban areas, especially in noise-saturated environments, contributing to an overall decrease in urban sound levels. However, this research highlights a downside of DC (Direct Current) fast charging stations: high-frequency noise emissions during fast charging, which can disturb nearby residents, especially in urban and residential areas. This noise, a result of the growing fast charging infrastructure, has led to complaints and even operational restrictions for some charging stations. Noise-related disturbances are a significant urban issue. The World Health Organization identifies noise as a key contributor to health burdens in Europe, even when noise annoyance is subjective, influenced by individual factors like sensitivity, genetics, and lifestyle, as well as by the specific environment. This paper analyzes the sound emission of a broad sample of DC fast charging stations from leading EU market brands. The goal is to provide tools that assist manufacturers, installers, and operators of rapid charging stations in mitigating the aforementioned sound emissions in order to align these infrastructures with Sustainable Development Goals 3 and 11 adopted by all United Nations Member States in 2015. Full article

Against the backdrop of a “dual-carbon” strategy, the use of photovoltaic storage charging stations (PSCSs), as an effective way to aggregate and manage electric vehicles, new energy sources, and energy storage, will be an important primary component of the electricity market. The operational characteristics of the aggregated resources within a PSCS determine its bidding space, which has an important influence on its bidding strategy. In this paper, a novel bidding space model is constructed for PSCSs, which dynamically integrates electric vehicles, photovoltaic generation, and energy storage. A two-stage bidding strategy for multiple PSCSs is established, with stage I aiming at achieving the lowest cost for the power purchased by a PSCS to optimize the power generation and power plan and stage II aiming at achieving the lowest cost of the grid operator’s power purchase to optimize the system’s power balance. Thirdly, the two-stage model is transformed into a single-layer, mixed-integer linear programming problem using dyadic theory and Karush–Kuhn–Tucker (KKT) conditions, enabling the derivation of the optimal bidding strategy. Finally, the example analysis verifies that the proposed model can achieve a reduction in the PSCS’s day-ahead power purchase cost and flexibly dispatch each resource within the PSCS to maximize revenue, as well as reducing power consumption behavior during peak tariff hours, to enhance the market power of the PSCS in the electricity market. Full article

The steering system plays a critical role in the vehicle’s handling and directly influences its lateral dynamics. Faults or abnormal behavior in this system can affect performance, cause vehicle instability, and even lead to accidents. Therefore, considering these potential events is essential for designing robust controllers for autonomous vehicles. For this reason, in this work, a fault-tolerant path-tracking Static Output-Feedback controller is designed to handle steering actuator faults in autonomous vehicle steering systems. The controller adopts a Linear Parameter Varying approach to effectively handle nonlinearities associated with varying vehicle speeds and tire behavior. Furthermore, it only uses information from sensors, avoiding estimation stages. This controller can operate in two modes: a no-fault mode where only the steering is controlled to follow the reference path and a fault mode where the controller manages both the steering and torque vectoring. In fault mode, torque vectoring compensates for faults in the steering actuator. The design of the controller is completed considering gain faults in the steering system. The simulation results show that the proposed controller successfully maintains vehicle stability and significantly reduces tracking errors during high-risk maneuvers, achieving reductions of up to 50.65% in lateral error and 47.26% in heading error under worst-case fault scenarios. Full article

This paper presents a carrier waves phase shifting method to reduce the dc-link capacitor current for a dual three-phase permanent magnet synchronous motor drive system. Dc-link capacitors absorb the ripple current generated at the input due to the harmonics of the pulse width modulation (PWM). The size, cost, reliability, and lifetime of the dc-link capacitor are negatively affected by this ripple current flowing through it. The proposed method is especially appropriate for common dc-link capacitors for a dual inverter system driving two PMSMs. In this paper, the input current of each inverter is analyzed using Double Fourier Analysis, and the harmonic components of the dc-link capacitor current are determined. The carrier wave phase shifting method is proposed to reduce the magnitude of the harmonics and thus reduce the dc-link capacitor current. Furthermore, the optimum angle between the carrier waves for the maximum reduction in the dc-link capacitor current is analyzed and simulated for different scenarios considering the speed and load torque of the PMSMs. The proposed method is verified through experiments and PMSMs are driven by three-phase voltage source inverters (VSIs) modulated with Space Vector Pulse Width Modulation (SVPWM), which is the most common PWM strategy. The proposed method reduces the dc-link capacitor current by 60%, thereby significantly decreasing the required dc-link capacitance, the volume of the drive system, and its cost. Full article

In order to improve the driving stability of distributed-drive intelligent electric vehicles under different roadway attachment conditions, this paper proposes a multi-parameter control algorithm based on the estimation of road adhesion coefficients. First, a seven-degree-of-freedom (7-DOF) vehicle dynamics model is established and optimized with a layered control strategy. The upper-level control module calculates the desired yaw rate and sideslip angle using the two-degree-of-freedom (2-DOF) vehicle model and estimates the road adhesion coefficient by using the singular-value optimized cubature Kalman filtering (CKF) algorithm; the middle-level utilizes the second-order sliding mode controller (SOSMC) as a direct yaw moment controller in order to track the desired yaw rate and sideslip angle while also employing a joint distribution algorithm to control the torque distribution based on vehicle stability parameters, thereby enhancing system robustness; and the lower-level controller performs optimal torque allocation based on the optimal tire loading rate as the objective. A Speedgoat-CarSim hardware-in-the-loop simulation platform was established, and typical driving scenarios were simulated to assess the stability and accuracy of the proposed control algorithm. The results demonstrate that the proposed algorithm significantly enhances vehicle-handling stability across both high- and low-adhesion road conditions. Full article

A smart regenerative braking system for EVs can reduce unnecessary brake operations by assisting in the braking of a vehicle according to the driving situation, road slope, and driver’s preference. Since the strength of regenerative braking is generally determined based on calibration data determined during the vehicle development process, some drivers could encounter inconveniences when the regenerative braking is activated differently from their driving habits. In order to solve this problem, various deep learning-based algorithms have been developed to provide driving stability by learning the driving data. Among those artificial intelligence algorithms, anomaly detection algorithms can successfully separate the deceleration data in abnormal driving situations, and the resulting refined deceleration data can be used to train the regression model to achieve better driving stability. This study evaluates the performance of a personalized driving assistance system by applying driver characteristic data, obtained through an anomaly detection algorithm, to vehicle control. Full article

With the widespread adoption of electric vehicles, the power converter, as a key component, plays a crucial role. Traditional fault detection methods often face challenges in real-time performance and computational efficiency, making it difficult to meet the demands of electric vehicle power converters for efficient and accurate fault diagnosis. To address this challenge, this paper proposes a novel fault detection model—SpikingShuffleNet. This paper first designs an efficient SpikingShuffle Unit that integrates grouped convolutions and channel shuffle techniques, effectively reducing the model’s computational complexity by optimizing feature extraction and channel interaction. Next, by appropriately stacking SpikingShuffle Units and refining the network architecture, a complete lightweight diagnostic network is constructed for real-time fault detection in electric vehicle power converters. Finally, the Mixed Local Channel Attention mechanism is introduced to address the potential limitations in feature representation caused by grouped convolutions, further enhancing fault detection accuracy and robustness by balancing local detail preservation and global feature integration. Experimental results show that SpikingShuffleNet exhibits excellent accuracy and robustness in the fault detection task for power converters, fulfilling the real-time fault diagnosis requirements for low-power embedded devices. Full article

Due to numerous distributed power sources connecting to the grid, which results in strong grid volatility and diminished power quality, the traditional energy storage configuration is limited in terms of flexibility and economy. Based on this, integrating electric vehicles (EVs) into the distribution network as energy storage devices has emerged as a promising development direction. This paper proposes a frequency-response optimization study considering the strong uncertainty model of EVs. First, from the perspective of temporal-spatial characteristics, energy storage resources, and users’ willingness to respond, the strong uncertainty model of EVs is constructed by fitting the trip chain and the access probability of their participation in energy storage. Second, the frequency optimization model is integrated and constructed according to the response capability of a single EV. Finally, examples and scenarios are analyzed to verify that the maximum and minimum frequency offsets are reduced by 69.41% and 66.69%, respectively, which significantly reduces frequency fluctuations and stabilizes the output of EV clusters. Full article

In its draft proposal for the Road Transport Act, the Croatian government referred to European Union Directive 2022/738, which concerns the use of hired vehicles for goods transport, rather than the pertinent European Union regulations on automated and autonomous vehicles, specifically Regulation 2019/2144 and Implementing Regulation 2022/1426. This oversight highlights Croatia’s lack of preparedness to integrate highly automated and autonomous vehicles, which are crucial for safety and environmental performance as per European Union standards. This paper aims to clarify the safety and legal recommendations for the trafficking of these vehicles in Croatia. Level 2 and Level 3 automated vehicles, present in smaller numbers in road traffic in Croatia, were compared from the perspective of the lack of driving tasks and its impact on driver safety. The stages of road liability for traffic accidents were also investigated, with recommendations of strict (default) liability of manufacturers for fully autonomous vehicles as well as presumed liability of all road traffic participants for highly automated vehicles. The safety and traffic benefits of possible infrastructure upgrades for highly automated and fully autonomous vehicles were discussed, mostly in the segment of dedicated lines. Full article

Densely populated urban megacities, such as the Metropolitan Area of Mexico City, face the ongoing deterioration of air quality. Emissions from industrail factories and internal combustion vehicles are the main sources of pollutants. We have evaluated different transition trends from internal combustion engine vehicles as bus, truck and van, and motorcycle to electric vehicles through 2050. The total vehicle growth follows a second-degree polynomial trend. Bus growth exhibits a linear trend. Truck and van growth display a second-degree polynomial trend. Motorcycle growth also follows a second-degree polynomial trend. We found that the most significant reductions in transportation emissions are observed in $C{O}_2$, followed by $N{O}_x$, volatile organic compound (VOC), and particulate matter, with light and heavy vehicles being the primary contributors to total emissions. Mexico City serves as a pilot laboratory where both the challenges and potential solutions to an issue affecting millions of citizens can be observed. If proven effective and practical, these solutions could be applied to other megacities. Full article

The transition to sustainable mobility is one of the most pressing and complex challenges for the automotive industry, with impacts that extend beyond the mere reduction of emissions. Electric vehicles, while at the center of this evolution, raise questions about the consumption of natural resources, such as lithium, copper, and cobalt, and their long-term sustainability. In addition, the introduction of advanced technologies, including artificial intelligence (AI) and autonomous systems, brings new challenges related to the management of components and materials needed for their production, creating a significant impact on supply chains. The growing demand for electric and autonomous vehicles is pushing the industry to rethink production models, favoring the adoption of circular economy principles to minimize waste and optimize the use of resources. To better understand the implications of this transition, this study adopts a multiple case study methodology, which allows in-depth exploration of different contexts and scenarios, and analysis of real cases of dismantling and recycling of internal combustion engines (ICEs) and electric vehicles (EVs). The research includes a financial simulation and a comparison of revenues from the dismantling of ICE and EV vehicles, highlighting differences in the value of recycled materials and the effectiveness of circular economy practices applied to the two types of vehicles. This approach provides a detailed overview of the economic benefits and challenges related to the management of the end of life of vehicles, helping to outline optimal strategies for a sustainable and cost-effective future in the automotive sector. Full article

A new graduate degree programme, Master of Science in Electric Vehicles (MScEV), for engineering students is presented, which is timely and vital for modern society. The purpose of this programme is to provide graduate students with up-to-date knowledge and skills that can enhance their career prospects in the fast-growing electric vehicle (EV) community. The programme not only provides technological knowledge in system design, operation, and management of EVs, but also involves research training in specific EV topics. This paper first outlines the rationale of the programme and reveals the shortcomings of existing EV education. Then, the curriculum structure of the newly developed MScEV programme as well as the corresponding core and elective courses are discussed. Finally, the findings of this programme are evaluated, indicating that the programme is attractive to an overwhelming number of students from diverse engineering backgrounds, as evidenced by the applicants’ and admittees’ degree qualifications and work experiences. Full article

This paper demonstrates the use of an Excel-based tool called the “Electric Vehicle-Charging Infrastructure Financial Analysis Spreadsheet Tool”, or “EVCI-FAST”, developed to analyze public–private partnership approaches to deliver publicly accessible EV-charging infrastructure that would not be commercially viable without a government subsidy. To demonstrate the use of this tool, we conducted a high-level screening analysis for a hypothetical bundle of publicly accessible EV-charging stations to assess the financial viability of delivering electric vehicle-charging infrastructure (EVCI) using alternative public–private partnership (P3) structures. This demonstration suggests that the EVCI-FAST could assist public agencies in determining whether their budgetary resources are adequate to support a proposed P3 for an EVCI project. The demonstration suggests that the EVCI-FAST could also help agencies decide which P3 structuring option would best meet their financial objectives. The results from the analysis of the hypothetical project suggest that public agencies could benefit considerably from a P3 structure that uses a minimum revenue guarantee to reduce revenue risk for the private partner. Full article

The adoption of a new technology is well described by an S-curve. It starts with a slow initial introduction, faster growth, and a final low-pace stage that corresponds to saturation. Once the innovation is introduced and progressively adopted, prior to saturation, some of the initial owners will begin selling their initially owned goods for different reasons, including lack of satisfaction, upgrading to a newer model, or other special unrevealed reasons. In a given market, new and second-hand products will coexist that will find new owners. The evolution of the two qualities of the same product will progress to a given equilibrium and a final ratio specific to each market. With the hypothesis of second-hand goods viewed as a new technology for lower budgets in the market, their adoption can also be described by the S-curve. The questions to be answered will relate to the dynamics of adoption of the two technologies, the ratio at equilibrium between new and used products in a market, and the delay required before equilibrium is achieved. In this manuscript, a realistic model is presented to approach and analyze the adoption of electric vehicles (EVs) with the mix of new and used vehicles with new registrations. The EV transition is presented with an adoption represented by the S-curve; the ratio of new to used EVs with new registrations is also presented in a context of high demand of used EVs and a context of rapid depreciation of EVs corresponding to lower demand of pre-owned EVs. The model predicts the number of years required before an equilibrium is reached in the ratio between used and new EVs in new registrations for a given market. Full article

The rapid proliferation of vehicles globally presents significant challenges to road transportation efficiency and safety, including accidents, emissions, energy utilization, and road management. Autonomous vehicle platooning emerges as a promising solution within intelligent transportation systems, offering benefits like reduced fuel consumption and emissions, and optimized road use. However, implementing autonomous vehicle platooning faces obstacles such as stability under disturbances, safety protocols, communication networks, and precise control. This paper proposes a novel control strategy coordinated Kalman observer–Linear Quadratic Regulator (CKO-LQR) to ensure platoon formation stability in the presence of disturbances. The disturbances considered include vehicle movements, sensor noise, and communication delays, with the leading vehicle’s movement serving as the commanding signal. The proposed controller maintains a constant inter-gap distance between vehicles despite the disturbances utilizing a coordinated Kalman observer to estimate preceding vehicle movements. A comparative analysis with conventional PID controllers demonstrates superior performance in terms of faster settling times and robustness against disturbances. This research contributes to enhancing the efficiency and safety of autonomous vehicle platooning systems. Full article

The positioning of lithium battery tabs in electric vehicles is a crucial aspect of the power battery assembly process. During the pre-tightening process of the lithium battery stack assembly, cells and foams undergo different deformations, leading to varying displacements of cells at different levels. Consequently, determining tab positions poses numerous challenges during the pre-tightening process of the stack assembly. To address these challenges, this paper proposes a method for detecting feature points and calculating the displacement of lithium battery stack tabs based on the MicKey method. This research focuses on the cell tab, utilizing the hue, saturation, and value (HSV) color space for image segmentation to adaptively extract the cell tab region and further obtain the ROI of the cell tab. In order to enhance the accuracy of tab displacement calculation, a novel method for feature point detection and displacement calculation of lithium battery stacks based on the MicKey (Metric Keypoints) method is introduced. MicKey can predict the coordinates of corresponding keypoints in the 3D camera space through keypoint matching based on neural networks, and it can acquire feature point pairs of the subject to be measured through its unique depth reduction characteristics. Results demonstrate that the average displacement error and root mean square error of this method are 0.03 mm and 0.04 mm, respectively. Compared to other feature matching algorithms, this method can more consistently and accurately detect feature points and calculate displacements, meeting the positioning accuracy requirements for the stack pole ear in the actual assembly process. It provides a theoretical foundation for subsequent procedures. Full article

Switched reluctance motors (SRMs) offer several advantages, including a magnet- and winding-free rotor, high mechanical strength, and exceptional output efficiency. However, the doubly salient pole structure and high-frequency switching power supply result in significant torque ripple and electromagnetic noise, which limit the application in the field of new energy vehicles. To address these issues, this paper proposes a direct instantaneous torque control (DITC) strategy based on an optimal switching angle torque sharing function (TSF). Firstly, an improved cosine TSF is designed to reasonably distribute the total reference torque among the phases, stabilizing the synthesized torque of SRM during the commutation interval. Subsequently, an improved artificial bee colony (ABC) algorithm is used to obtain the optimal switching angle data at various speeds, integrating these data into the torque distribution module to derive the optimal switching angle model. Finally, the effectiveness of the proposed control strategy is validated through simulations of an 8/6-pole SRM. Simulation results demonstrate that the proposed control strategy effectively suppresses torque ripple during commutation and reduces the peak current at the beginning of phase commutation. Full article

Many modern electric drives for cars, trucks, ships, etc., use permanent magnet synchronous motors because of their compact size. At the same time, permanent magnets are expensive, and their uncontrolled flux is a problem when it is necessary to provide a wide constant power speed range in the field weakening region. An alternative to permanent magnet motors is synchronous motors with field windings. This article presents a novel design of a traction brushless synchronous motor with a field winding and a two-phase harmonic exciter winding on the rotor and zero-sequence signal injection. The two-phase harmonic exciter winding increases the electromotive force on the field winding compared to a single-phase one and makes it possible to start the motor at any rotor position. This article discusses the advantages of the proposed design over conventional solutions. A simplified mathematical model based on the finite element method for steady state simulation is presented. The machine performance of a hysteresis current controller and a field-oriented PI current controller are compared using the model. Full article