World Electr. Veh. J., Volume 16, Issue 7 (July 2025) – 61 articles

In recent years, the rapid spread of electric vehicles (EVs) has intensified the competition to develop power units for EVs. In particular, improving the driving range of EVs has become a major topic, and in order to achieve this, many studies have been conducted on improving the efficiency of EV power units. In this study, we propose a multiple-inverter-drive permanent magnet synchronous motor based on an 8-pole, 48-slot structure, which is commonly used as an EV motor. The proposed motor is composed of two completely independent parallel inverters and windings, and intermittent operation is possible; that is, only one inverter and one parallel winding is used depending on the situation. In the proposed motor, we compare losses including stator iron loss, rotor iron loss, and magnet eddy current loss by PWM voltage inputs for some stator winding topologies, we show that the one-side winding arrangement is the most efficient during intermittent operation, and that it is more efficient than normal operation especially in the low-speed, low-torque range. Finally, through a vehicle-driving simulation considering the efficiency map including motor loss and inverter loss, we show that the intentional use of intermittent operation can improve electrical energy consumption. Full article

Electric vehicles (EVs) have been the most dependable and feasible choice for decarbonizing road transport over the last decade. To ensure the advancement of EVs and establish them as a sustainable alternative to internal combustion engine (ICE) vehicles, the EV sector and technological growth have largely relied on government subsidies. A significant challenge for EVs is their faster depreciation compared to ICE vehicles, primarily owing to swift technological advancements that propel the market while simultaneously rendering older EV models outdated too soon. Another factor that leads to the quicker depreciation of EVs is subsidies. The anticipated cessation of subsidies is expected to provide the required leverage to mitigate the rapid value decline in EVs, given the larger price disparity between new and used EVs. Batteries, which enable EVs to be a viable option, significantly contribute to the depreciation of EVs. In addition to the potential decline in EV battery performance, advancements in technology and reduced prices provide newer models with improved range at a more affordable cost. The used EV market accurately represents the rapid devaluation of EVs; consequently, the two topics are tightly related. Though it might not be immediately apparent, it seems evident that the pace of depreciation of EVs significantly contributes to the small size of the second-hand EV market. Depreciation is a key factor influencing the used EV market. This manuscript outlines the key aspects of depreciation and sustainability in the EV transition, especially those linked to rapid technological advancements, such as batteries, in addition to subsidies and the used EV market. The objective of this manuscript is to expose and analyze the relation between the drivers of the second-hand EV market, such as the cost of ownership, technology, and subsidies, and, on the other hand, present the interplay perspectives and challenges. Full article

The growing demand for reliable and secure vehicle-to-vehicle (V2V) communication in next-generation intelligent transportation systems has accelerated the adoption of reconfigurable intelligent surfaces (RIS) as a means of enhancing link quality, spectral efficiency, and physical layer security. In this paper, we investigate the problem of secrecy rate maximization in a cooperative dual-RIS-aided V2V communication network, where two cascaded RISs are deployed to collaboratively assist with secure data transmission between mobile vehicular nodes in the presence of eavesdroppers. To address the inherent complexity of time-varying wireless channels, we propose a novel evolutionary transformer-gated recurrent unit (Evo-Transformer-GRU) framework that jointly learns temporal channel patterns and optimizes the RIS reflection coefficients, beam-forming vectors, and cooperative communication strategies. Our model integrates the sequence modeling strength of GRUs with the global attention mechanism of transformer encoders, enabling the efficient representation of time-series channel behavior and long-range dependencies. To further enhance convergence and secrecy performance, we incorporate an improved gray wolf optimizer (IGWO) to adaptively regulate the model’s hyper-parameters and fine-tune the RIS phase shifts, resulting in a more stable and optimized learning process. Extensive simulations demonstrate the superiority of the proposed framework compared to existing baselines, such as transformer, bidirectional encoder representations from transformers (BERT), deep reinforcement learning (DRL), long short-term memory (LSTM), and GRU models. Specifically, our method achieves an up to 32.6% improvement in average secrecy rate and a 28.4% lower convergence time under varying channel conditions and eavesdropper locations. In addition to secrecy rate improvements, the proposed model achieved a root mean square error (RMSE) of 0.05, coefficient of determination ($R^2$) score of 0.96, and mean absolute percentage error (MAPE) of just 0.73%, outperforming all baseline methods in prediction accuracy and robustness. Furthermore, Evo-Transformer-GRU demonstrated rapid convergence within 100 epochs, the lowest variance across multiple runs. Full article

The integration of shared autonomous vehicles into historic urban areas presents both opportunities and challenges. In heritage-rich environments like very old Asian (such as Suzhou old town, which serves as a use case example) or European (especially Mediterranean coastal cities) areas—characterized by narrow alleys, dense development, and sensitive cultural landscapes—shared autonomous vehicle adoption raises critical spatial and social questions. This study employs a qualitative, user-centered approach based on the ripple model to examine residents’ perceptions across four dimensions: residential patterns, parking land use, regional accessibility, and street-level infrastructure. Semi-structured interviews with 27 participants reveal five key findings: (1) public trust depends on transparent decision-making and safety guarantees; (2) shared autonomous vehicles may reshape generational residential clustering; (3) the short-term parking demand remains stable, but the long-term reuse of space is feasible; (4) shared autonomous vehicles could enhance accessibility in historic cores; (5) transport systems may evolve toward intelligent, human-centered designs. Based on these insights, the study proposes three strategies: (1) transparent risk assessment using explainable artificial intelligence and digital twins; (2) polycentric development to diversify land use; (3) hierarchical street retrofitting to balance mobility and preservation. While this study is limited by its qualitative scope and absence of simulation, it offers a framework for culturally sensitive, small-scale interventions supporting sustainable mobility transitions in historic urban contexts. Full article

Collision avoidance between vehicles is a great challenge, especially in the context of mixed driving of connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs). Advances in automation and connectivity technologies provide opportunities for CAVs to drive cooperatively. This paper proposes a proactive collision avoidance model, aiming to avoid collisions by controlling the speed and lane-changing behavior of CAVs. In the model, the subject vehicle first collects information about surrounding lanes and judges the traffic conditions; it then chooses to decelerate or change lanes to avoid collisions. The subject vehicle also searches for the optimal vehicle in the surrounding lanes for cooperation. The effectiveness of the proposed collision avoidance model is verified through the Python-SUMO platform. The experimental results show that the performance of the collision avoidance model is better than that of the cooperative adaptive cruise control (CACC) model in terms of average speed, lost time and the number of vehicle conflicts, proving the advantages of the proposed model in safety and efficiency. Full article

Due to the absence of engine noise in new energy vehicles, road noise and wind noise become particularly noticeable. Therefore, studying the noise transmission through car doors is essential to effectively reduce the impact of these noises on the passenger compartment. To address the optimization of the sound absorption performance of single-layer porous plates combined with lightweight glass wool used in the doors of electric vehicles, this study established a microscopic acoustic performance analysis model based on the transfer matrix method and sound transmission loss theory. The effects of medium type, perforation rate, perforation radius, material thickness, and porosity on the sound absorption coefficient, impedance characteristics, and reflection coefficient were systematically investigated. Results indicate that in the high-frequency range (above 1200 Hz), the sound absorption coefficients of both rigid and flexible media can reach up to 0.9. When the perforation rate increases from 0.01 to 0.2, the peak sound absorption coefficient in the high-frequency band (1400–2000 Hz) rises from 0.45 to 0.85. Increasing the perforation radius to 0.03 m improves acoustic impedance matching. This research provides theoretical support and a parameter optimization basis for the design of acoustic packaging materials for electric vehicles, contributing significantly to enhancing the interior acoustic environment. Full article

Free stroke, which means the intermittent no-load operation state of dampers, can cause an abnormal noise and unavoidably lead to the deterioration of vehicle NVH performance. In electric vehicles, the noise is particularly intolerable because there are no engine sounds to mask it. Focusing on this, the mechanism of the free-stroke phenomenon is analyzed. A method, which involves parametric models and numerical simulation, is proposed to prevent free-stroke phenomena during the damper design phase. This paper proposes a free-stroke mechanism based on a fluid–structure interaction (FSI) numerical method, combined with experiments, which intends to provide a design reference with guaranteed performance for dampers. Initially, according to parametric cavitation models and by applying numerical methods, simulations for the proposed FSI model are calculated. By analyzing the simulation results, strain variation characteristics near the bottom of the damper valves are revealed, which establish the relationships between strain change, cavitation and the free-stroke phenomena. Meanwhile, the specific position and distribution of free-stroke failure are clearly located by running diverse loading speeds. Finally, all the theoretical analysis results are verified using damper noise tests and indicator bench tests. Full article

This paper presents the results of the mathematical modeling and experimental studies of charging a traction lithium-ion battery of a passenger electric car using an integrated charger based on a traction voltage inverter. An original three-stage charging algorithm (3PT/PN) has been developed and implemented, which provides a sequential decrease in the charging current when the specified voltage and temperature levels of the battery module are reached. As part of this study, a comprehensive mathematical model has been created that takes into account the features of the power circuit, control algorithms, thermal effects and characteristics of the storage battery. The model has been successfully verified based on the experimental data obtained when charging the battery module in real conditions. The maximum error of voltage modeling has been 0.71%; that of current has not exceeded 1%. The experiments show the achievement of a realized capacity of 8.9 Ah and an integral efficiency of 85.5%, while the temperature regime remains within safe limits. The proposed approach provides a high charge rate, stability of the thermal state of the battery and a long service life. The results can be used to optimize the charging infrastructure of electric vehicles and to develop intelligent battery module management systems. Full article

With the rapid development of new energy vehicles, the echelon utilization of power batteries has become a key pathway to promoting efficient resource recycling and environmental sustainability. To address the limitation of the existing studies that overlook the dynamic strategic interactions among multiple stakeholders, this paper constructs a tripartite evolutionary game model involving the government, battery recycling enterprises, and consumers. By incorporating consumers’ battery usage levels into the strategy space, the model captures the behavioral evolution of all these parties under bounded rationality. Numerical simulations are conducted to analyze the impact of government incentives and penalties, consumer usage behaviors, and enterprise recycling modes on system stability. The results show that a “low-subsidy, high-penalty” mechanism can more effectively guide enterprises to prioritize echelon utilization and that moderate consumer usage significantly improves battery reuse efficiency. This study enriches the application of the evolutionary game theory in the field of battery recycling and provides quantitative evidence and practical insights for policy formulation. Full article

In this paper, a detailed review of electric vehicle (EV) charging station architectures is first presented, and then an optimal architecture suitable for a large MW-scale EV fast-charging station (EVFS) with multiple fast chargers is proposed and evaluated. The study examines various EVFS architectures, including those currently deployed in commercial sites. Most EVFS implementations use either a common AC-bus or a common DC-bus configuration, with DC-bus architectures being slightly more predominant. The paper analyzes the EV charging and battery energy storage system (BESS) requirements for future large-scale EVFSs and identifies key implementation challenges associated with the full adoption of the common DC-bus approach. To overcome these limitations, a novel multi-zone EVFS architecture is proposed that employs an optimal combination of isolated and non-isolated DC-DC converter topologies while maintaining galvanic isolation for EVs. The system efficiency and total power converter capacity requirements of the proposed architecture are evaluated and compared with those of other EVFS models. A major feature of the proposed design is its multi-zone division and zonal isolation capabilities, which are not present in conventional EVFS architectures. These advantages are demonstrated through a scaled-up model consisting of 156 EV fast chargers. The analysis highlights the superior performance of the proposed multi-zone EVFS architecture in terms of efficiency, total power converter requirements, fault tolerance, and reduced grid impacts, making it the best solution for reliable and scalable MW-scale commercial EVFS systems of the future. Full article

Autonomous vehicles are expected to reduce traffic accident casualties, as driver distraction accounts for 90% of accidents. These vehicles rely on sensors and controllers to operate independently, requiring robust security mechanisms to prevent malicious takeovers. This research proposes a novel approach to assessing the impact of cyber-attacks on autonomous vehicles and their surroundings, with a strong focus on prioritizing human safety. The system evaluates the severity of incidents caused by attacks, distinguishing between different events—for example, a pedestrian injury is classified as more critical than a collision with an inanimate object. By integrating deep neural network technology with feature engineering, the proposed system provides a comprehensive impact assessment. It is validated using metrics such as MAE, loss function, and Spearman’s correlation through experiments on a dataset of 5410 samples. Beyond enhancing autonomous vehicle security, this research contributes to real-world attack impact assessment, ensuring human safety remains a priority in the evolving autonomous landscape. Full article

This paper provides a comprehensive review of the development, deployment and challenges of Intelligent Transport Systems (ITSs) in Europe. Driven by the EU Directive 2010/40/EU, the deployment of ITSs has become essential for improving the safety, efficiency and sustainability of transport. The study examines how ITS technologies, such as automation, real-time traffic data analytics and vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, have been integrated to improve urban mobility and road safety. In addition, it reviews significant European initiatives and case studies from several cities, which show visible improvements in reducing congestion, reducing CO₂ emissions and increasing the use of public transport. The paper highlights, despite progress, major obstacles to widespread adoption, such as technical interoperability, inadequate regulatory frameworks and insufficient data sharing between stakeholders. These issues prevent ITS applications from scaling up and functioning well in EU Member States. To overcome these problems, the study highlights the need for common standards and cooperation frameworks. The research analyses the laws, technological developments and socio-economic effects of ITSs. By promoting sustainable and inclusive mobility, ITSs can contribute to the European Green Deal and climate goals. Finally, the paper presents ITSs as a revolutionary solution for future European transport systems and offers suggestions to improve their interoperability, data governance and policy support. Full article

In response to the problem of cooperative steering control between drivers and intelligent driving systems, a master–slave Game-Based human–machine cooperative steering control framework with adaptive weight fuzzy decision-making is constructed. Firstly, within this framework, a dynamic weight approach is established. This approach takes into account the driver’s state, traffic environment risks, and the vehicle’s global control deviation to adjust the driving weights between humans and machines. Secondly, the human–machine cooperative relationship with unconscious competition is characterized as a master–slave game interaction. The cooperative steering control under the master–slave game scenario is then transformed into an optimization problem of model predictive control. Through theoretical derivation, the optimal control strategies for both parties at equilibrium in the human–machine master–slave game are obtained. Coordination of the manipulation actions of the driver and the intelligent driving system is achieved by balancing the master–slave game. Finally, different types of drivers are simulated by varying the parameters of the driver models. The effectiveness of the proposed driving weight allocation scheme was validated on the constructed simulation test platform. The results indicate that the human–machine collaborative control strategy can effectively mitigate conflicts between humans and machines. By giving due consideration to the driver’s operational intentions, this strategy reduces the driver’s workload. Under high-risk scenarios, while ensuring driving safety and providing the driver with a satisfactory experience, this strategy significantly enhances the stability of vehicle motion. Full article

The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks. Full article

Reverse engineering of internal vehicle communication is a crucial discipline in vehicle benchmarking. The process presents a time-consuming procedure associated with high manual effort. Car manufacturers use unique signal addresses and encodings for their internal data. Accessing this data requires either expensive tools suitable for the respective vehicles or experienced engineers who have developed individual approaches to identify specific signals. Access to the internal data enables reading the vehicle’s status, and thus, reducing the need for additional test equipment. This results in vehicles closer to their production status and does not require manipulating the vehicle under study, which prevents affecting future test results. The main focus of this approach is to reduce the cost of such analysis and design a more efficient benchmarking process. In this work, we present a methodology that identifies signals without physically manipulating the vehicle. Our equipment is connected to the vehicle via the On-Board Diagnostics (OBD)-II port and uses the Unified Diagnostics Service (UDS) protocol to communicate with the vehicle. We access, capture, and analyze the vehicle’s signals for future analysis. This is a holistic approach, which, in addition to decoding the signals, also grants access to the vehicle’s data, which allows researchers to utilize state-of-the-art methodologies to analyze their vehicles under study by greatly reducing necessary experience, time, and cost. Full article

Due to the limitations of current battery technologies—such as lower energy density and high cost compared to fossil fuels—electric vehicles (EVs) face constraints in applications requiring extended range or heavy payloads, such as refuse trucks. As a midterm solution, hybrid electric vehicles (HEVs) combine internal combustion engines (ICEs) and electric powertrains to enable flexible energy usage, particularly in urban duty cycles characterized by frequent stopping and idling. This study introduces a model-based energy management strategy using the Equivalent Consumption Minimization Strategy (ECMS), tailored for a retrofitted series hybrid refuse truck. A conventional ISUZU NPR 10 truck was instrumented to collect real-world driving and operational data, which guided the development of a vehicle-specific ECMS controller. The proposed strategy was evaluated over five driving cycles—including both standardized and measured urban scenarios—under varying load conditions: Tare Mass (TM) and Gross Vehicle Mass (GVM). Compared with a rule-based control approach, ECMS demonstrated up to 14% improvement in driving range and significant reductions in exhaust gas emissions (CO, NO_x, and CO₂). The inclusion of auxiliary load modeling further enhances the realism of the simulation results. These findings validate ECMS as a viable strategy for optimizing fuel economy and reducing emissions in hybrid refuse truck applications. Full article

Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function $g\left(n\right)$, and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function $h\left(n\right)$. Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration. Full article

The rapid growth of electric vehicles (EVs) poses significant challenges to the safe operation of charging stations and distribution networks. Variations in charging power across different EV manufacturers lead to substantial load fluctuations at charging stations. In some tourist cities in China, charging loads can surge at specific times, yet existing research mainly focuses on optimizing station location and basic capacity configuration, neglecting sudden peak load management. To address this, we propose a method that enhances charging station carrying capacity (CSCC) by coordinating multi-flexibility resources. This method optimizes the configuration of soft open points (SOPs) to enable flexible interconnections between feeders and incorporates elastic load scheduling for data centers. An optimization model is developed to coordinate these flexible resources, thereby improving the CSCC. Case studies demonstrate that this approach effectively increases CSCC at lower costs, facilitates the utilization of renewable energy, and enhances the overall system economy. The results validate the feasibility and effectiveness of the proposed approach, offering new insights for urban grid planning and EV charging stations optimization. Full article

The transportation sector in South Asia largely depends on internal combustion engine (ICE) vehicles, which are responsible for a large share of greenhouse gas (GHG) emissions, air pollution, and the increase in fuel prices. Although hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and fully electric vehicles (EVs) constitute promising alternatives, the rate of their implementation is low due to factors such as the high initial investment, the absence of the required infrastructure, and the reliance on fossil fuel-based electricity. This study is the first of its kind to examine Bangladesh’s drivetrain options in a comprehensive way, with in-depth real-world emission testing and economic analysis as the main tools of investigation into the environmental and economic feasibility of different technologies used in the vehicles available in Bangladesh, including lifecycle costs and infrastructure constraints. The study findings have shown that hybrid and plug-in hybrid vehicles are the best options, since they have moderate emissions and cost efficiency, respectively. Fully electric vehicles, however, face two main challenges: the overall lack of charging infrastructure and the overall high purchase prices. Among the evaluated technologies, PHEVs exhibited the lowest environmental and economic burden. The Toyota Prius PHEV emitted 98% less NOx compared to the diesel-powered Pajero Sport and maintained the lowest per-kilometer cost at BDT 6.39. In contrast, diesel SUVs emitted 178 ppm NOx and cost 22.62 BDT/km, reinforcing the transitional advantage of plug-in hybrid technology in Bangladesh’s context. Full article

This paper presents a hybrid fast charging strategy for static wireless power transfer (WPT) systems that synergistically combines pulsed current and multi-stage current (MCM) modes to enable rapid yet battery-health-conscious electric vehicle (EV) charging, thereby promoting sustainable transportation. The proposed approach employs a memetic algorithm (MA) to dynamically optimize the charging parameters, achieving an optimal balance between speed and battery longevity while maintaining 90.78% system efficiency at the SAE J2954-standard 85 kHz operating frequency. A neural-network-based state of charge (SOC) estimator provides accurate real-time monitoring, complemented by MA-tuned PI control for enhanced resonance stability and adaptive pulsed current–MCM profiles for the optimal energy transfer. Simulations and experimental validation demonstrate faster charging compared to that using the conventional constant current–constant voltage (CC-CV) methods while effectively preserving the battery’s state of health (SOH)—a critical advantage that reduces the environmental impact of frequent battery replacements and minimizes the carbon footprint associated with raw material extraction and battery manufacturing. By addressing both the technical challenges of high-power WPT systems and the ecological imperative of battery preservation, this research bridges the gap between fast charging requirements and sustainable EV adoption, offering a practical solution that aligns with global decarbonization goals through optimized resource utilization and an extended battery service life. Full article

Urban bus fleets worldwide face urgent decarbonization requirements, with Germany targeting net-zero emissions by 2050. Current electrification research often addresses individual components—energy consumption, scheduling, or charging infrastructure—in isolation, lacking integrated frameworks that capture complex system interactions. This study presents “eflips-X”, a modular, open-source simulation framework that integrates energy consumption modeling, battery-aware block building, depot–block assignment, terminus charger placement, depot operations simulation, and smart charging optimization within a unified workflow. The framework employs empirical energy models, graph-based scheduling algorithms, and integer linear programming for depot assignment and smart charging. Applied to Berlin’s bus network—Germany’s largest—three scenarios were evaluated: maintaining existing blocks with electrification, exclusive depot charging, and small batteries with extensive terminus charging. Electric fleets need 2.1–7.1% additional vehicles compared to diesel operations, with hybrid depot-terminus charging strategies minimizing this increase. Smart charging reduces peak power demand by 49.8% on average, while different charging strategies yield distinct trade-offs between infrastructure requirements, fleet size, and operational efficiency. The framework enables systematic evaluation of electrification pathways, supporting evidence-based planning for zero-emission public transport transitions. Full article

This study presents an adaptive integrated chassis control strategy for enhancing vehicle stability under different road conditions, specifically through the real-time estimation of tire cornering stiffness. A hierarchical control architecture is developed, combining active front steering (AFS) and direct yaw moment control (DYC). A recursive regularized weighted least squares algorithm is designed to estimate tire cornering stiffness from measurable vehicle states, eliminating the need for additional tire sensors. Leveraging this estimation, an adaptive sliding mode controller (ASMC) is proposed in the upper layer, where a novel self-tuning mechanism adjusts control parameters based on tire saturation levels and cornering stiffness variation trends. The lower-layer controller employs a weighted least squares allocation method to distribute control efforts while respecting physical and friction constraints. Co-simulations using MATLAB 2018a/Simulink and CarSim validate the effectiveness of the proposed framework under both high- and low-friction scenarios. Compared with conventional ASMC and DYC strategies, the proposed controller exhibits improved robustness, reduced sideslip, and enhanced trajectory tracking performance. The results demonstrate the significance of the real-time integration of tire dynamics into chassis control in improving vehicle handling and stability. Full article

Lithium-polymer (LiPo) batteries are valued for their high energy density, stable voltage output, low self-discharge, and strong reliability, making them a popular choice for high-performance and portable applications. Despite these advantages, the charging behavior of LiPo batteries—especially during rapid charging—remains an area with limited understanding. This research examines the electro-thermal characteristics of VARTA LiPo batteries when subjected to high charging currents (2C, 3C, and 4C rates). A temperature-sensitive charging model is developed to address safety and efficiency concerns during fast charging. Experimental data indicate that charging at 45 °C yields the best performance, achieving 80% state of charge (SoC) within 25 min. However, charging at temperatures above or below this level (such as 25 °C) reduces efficiency due to increased internal resistance and accelerated battery aging. The model, validated across a range of temperatures (25 °C, 35 °C, 45 °C, and 60 °C), shows that longer constant-current (CC) charging phases at higher temperatures are associated with lower internal resistance. These results highlight the importance of effective thermal management for optimizing both safety and performance in LiPo battery applications. Full article

The increasing adoption of electric vehicles (EVs) presents a growing demand for efficient, real-time battery monitoring systems. Many existing Battery Management Systems (BMS) with built-in Controller Area Network (CAN) communication are often expensive or lack user-friendly interfaces for displaying data. Moreover, integrating such BMS units with standard Human–Machine Interface (HMI) displays remains a challenge in cost-sensitive applications. This article presents the design and development of an interface for integrating the BMS of electric vehicles with the ATD3.5-S3 display using the CANopen protocol. The system enables the real-time visualization of essential battery parameters, including voltage, current, temperature, and state of charge (SOC) percentage. The proposed system utilizes a JK BMS, an ESP32 microcontroller, and a TJA1051 CAN transceiver to convert UART data into CAN Open messages. The design emphasizes affordability, modular communication, and usability in EV applications. Testing under various load conditions confirms the system’s stability, reliability, and suitability for practical use in electric vehicles. Full article

Autonomous valet parking technology refers to a vehicle’s use of onboard sensors and line-controlled chassis to carry out a fully automatic valet parking function, which can greatly improve the driver’s experience. This study focuses on autonomous parking, employing environmental modeling and vehicle kinematics models. Innovatively applying the PSBi-RRT algorithm to path planning in autonomous parking systems constitutes this research’s contribution to this field. Firstly, the environment is modelled by the raster method; then, the PSBi-RRT algorithm is used for path planning, and a B-spline curve is used for path optimization. Speed and acceleration are smoothed at the same time, and finally, a smooth and obstacle-avoiding path planning scheme is obtained. The results show that an autonomous parking system based on the PSBi-RRT algorithm performs path planning from the vehicle to the parking space. Compared to RRT and Bi-RRT, PSBi-RRT generates shorter planning paths, smoother heading angle changes, shorter planning times, fewer nodes, and higher success rates. This research provides theoretical support for the development of autonomous parking technology. Full article

This study aims to evaluate the feasibility of replacing internal combustion vehicles (ICVs) with homologated electric vehicles (EVs) within Ecuador’s electricity supply companies, using a structured methodology to ensure operational efficacy and emissions reduction. This was carried out by considering a methodology that allows standardized decision criteria for replacement through determining specific requirements, contrasting technical characteristics, and estimating emissions reduction without compromising the development of transportation daily activities within the companies. The results showed that there are three main categories of combustion-powered vehicles that have electric counterparts, for they are suitable to be replaced under certain operation parameters with a significant reduction in the annual CO₂ emissions of around 85%. However, considering market availability and technical constraints, a realistic migration scenario suggests 56% reduction in CO₂ emissions. Electric mobility presents a compelling opportunity for decarbonization; achieving true sustainability will require the continued diversification and decarbonization of the national electricity supply, given that 90% of electricity production is based on renewable energy. Full article

This study resolves a critical challenge in electromechanical brake system validation: conventional ABS/RBS integrated platforms’ inability to dynamically simulate tire-road adhesion characteristics during braking. We propose a fuzzy adaptive PID-controlled magnetic powder clutch (MPC) system that achieves ground braking force simulation synchronized with slip ratio variations. The innovation encompasses: (1) Dynamic torque calculation model incorporating the curve characteristics of longitudinal friction coefficient ($\phi$) versus slip ratio ($s$), (2) Nonlinear compensation through fuzzy self-tuning PID control, and (3) Multi-scenario validation platform. Experimental validation confirms superior tracking performance across multiple scenarios: (1) Determination coefficients R² of 0.942 (asphalt), 0.926 (sand), and 0.918 (snow) for uniform surfaces, (2) R² = 0.912/0.908 for asphalt-snow/snow-asphalt transitions, demonstrating effective adhesion characteristic simulation. The proposed control strategy achieves remarkable precision improvements, reducing integral time absolute error (ITAE) by 8.3–52.8% compared to conventional methods. Particularly noteworthy is the substantial ITAE reduction in snow conditions (236.47 vs. 500.969), validating enhanced simulation fidelity under extreme road surfaces. The system demonstrates consistently rapid response times. These improvements allow for highly accurate replication of dynamic slip ratio variations, establishing a refined laboratory-grade solution for EV regenerative braking coordination validation that greatly enhances strategy optimization efficiency. Full article

Purely electric and hybrid vehicles are emerging as the transport sector’s response to meet climate goals, aiming to mitigate global warming. As the adoption of transport electrification increases, the importance of recycling components of the electric propulsion system at the end of their life grows, particularly the battery pack, which significantly contributes to the vehicle’s final cost and generates environmental impacts and CO₂ during production. This work presents an overview of the recycling processes for lithium-ion automotive batteries, emphasizing the developing Brazilian scenario and more established international scenarios. In Brazil, companies and research centers are investing in recycling and using reused cathode material to manufacture new batteries through the hydrometallurgical process. On the international front, pyrometallurgy and physical recycling are being applied, and other methods, such as direct processes and biohydrometallurgy, are also under study. Regardless of the recycling method, the main challenge is scaling prototype processes to meet current and future battery demand, driven by the growth of electric and hybrid vehicles, pursuing both environmental gains through reduced mining and CO₂ emissions and economic viability to make recycling profitable and support global electrification. Full article

This research introduces a comprehensive AI-driven framework for secure and efficient load management in multi-station electric vehicle (EV) charging networks, responding to the increasing demand and operational difficulties associated with widespread EV adoption. The suggested architecture has three main parts: a Smart Load Balancer (SLB), an AI-driven intrusion detection system (AIDS), and a Real-Time Analytics Engine (RAE). These parts use advanced machine learning methods like Support Vector Machines (SVMs), autoencoders, and reinforcement learning (RL) to make the system more flexible, secure, and efficient. The framework uses federated learning (FL) to protect data privacy and make decisions in a decentralized way, which lowers the risks that come with centralizing data. The framework makes load distribution 23.5% more efficient, cuts average wait time by 17.8%, and predicts station-level demand with 94.2% accuracy, according to simulation results. The AI-based intrusion detection component has precision, recall, and F1-scores that are all over 97%, which is better than standard methods. The study also finds important gaps in the current literature and suggests new areas for research, such as using graph neural networks (GNNs) and quantum machine learning to make EV charging infrastructures even more scalable, resilient, and intelligent. Full article

In light of increasing environmental issues, green and environmentally friendly growth has emerged as a global consensus., making the progression of a low-carbon and eco-friendly new energy vehicle (NEV) industry essential for countries globally. This study focuses on the 26 provinces of China, employing benchmark regression, mediation analysis, spatial econometrics, and difference-in-differences models to comprehensively investigate the impact and underlying mechanisms of NEV industry agglomeration on high-quality green development, using a unified framework to measure both agglomeration and development standards, which enhances the accuracy of previous measurements using a single indicator. The findings show that NEV industry agglomeration directly promotes high-quality green development, mediated significantly by green technological innovation and public environmental awareness. Analysis reveals significant regional heterogeneity, with stronger NEV industry agglomeration in midwestern regions, areas prioritizing sustainable and low-carbon policies, and regions with advanced economic and financial systems, leading to a greater positive impact on high-quality green development. NEV industry agglomeration influences high-quality green development in neighboring regions through spatial spillover effects. The results remain robust when using instrumental variables and treating NEV-related policy formulation as a quasi-natural experiment. This study provides theoretical guidance and policy recommendations to encourage high-quality green development through NEV industry agglomeration. Full article