World Electr. Veh. J., Volume 16, Issue 3 (March 2025) – 73 articles

Under the low carbon goal, recycling power batteries (PBs) from new energy vehicles (NEVs) is a crucial measure to address resource shortages and reduce carbon emissions. This study examined the insufficient collaboration among the responsible entities and the imperfections in market mechanisms within the PB recycling system. We overcome the limitations of traditional tripartite evolutionary game models by developing a four-party evolutionary game model that incorporates the government, manufacturers, recyclers, and consumers to investigate the strategic interactions within the extended producer responsibility (EPR) framework. Using MATLAB 2023a numerical simulations and Lyapunov stability analysis, we found that the system’s stability and efficiency depend on stakeholder collaboration and effective government policy guidance. The system evolves toward a Pareto optimal state when all parties adopt proactive recycling strategies. Meanwhile, ensuring substantial profits for manufacturers and recyclers is critical for the feasibility and stable operation of compliant recycling channels. While manufacturers and recyclers are more sensitive to subsidies than consumers, consumer decision-making is key to market stability. Long-term excessive subsidies may lead to diminishing marginal benefits. Strategic recommendations are provided for policymakers and stakeholders to enhance the efficiency and sustainability of the PB recycling system. Full article

The driving power of hybrid electric vehicles serves as a crucial foundation for optimizing energy management strategies. The substantial load carried by heavy-duty vehicles significantly impacts the driving power through slope and acceleration. To minimize energy consumption in heavy-duty series hybrid electric vehicles, key variables are identified and predicted individually, employing the predictive equivalent energy consumption minimization strategy (ECMS) to optimize power distribution. In order to accurately forecast the driving power of heavy-duty vehicles, the vehicle mass is determined using the least squares method. To enhance time series data forecasting capabilities, a CNN-LSTM hybrid network is utilized to predict future vehicle speed and road slope based on historical time series data. By applying a longitudinal dynamics model, the identified vehicle weight, predicted speed, and slope can be converted into actual vehicle driving power. Within the prediction timeframe, different rolling calculation energy distribution methods utilizing equivalent factors are employed to achieve optimal energy consumption reduction. Road experiment data demonstrate that identification errors for various vehicle weights remain below 3%. The average RMSE for single-step drive power prediction stands at 14.8 kW. Simulation results using a test road reveal that the predictive ECMS reduces energy consumption by 6.2% to 15% compared to the original rule-based strategy. Full article

The rapid development of electric vehicles (EVs) around the world has resulted in new challenges for road safety. Identifying the features of EV crashes is a precondition for developing effective countermeasures. However, due to the short history of EV development, existing studies on EV crashes are quite limited. China, which has the largest EV market in the world, has witnessed a substantial increase in EV crashes in recent years. Therefore, this study comprehensively investigated the characteristics of EV crashes by analyzing the 2023 traffic crash data from Changshu. This is a pioneering study that discusses EV safety by comparing real EV crashes and ICEV crashes from a city in China, the largest EV market in the world. It was found that EV crashes had a higher fatality rate compared to internal combustion engine vehicle (ICEV) crashes. Compared to ICEV crashes, EV crashes are more likely to hit pedestrians and occur during the starting phase. Among the vehicles involved in crashes, the proportion of EVs used for passenger and freight transport was higher than that of ICEVs. In addition, for EV crashes, the proportion of female drivers was much higher, but the proportion of elderly drivers was much lower. Thus, to identify the significant factors influencing crash severity, a logistic regression model was built. The results confirm that EV crashes are more likely to be more fatal than ICEV crashes. In addition, hitting pedestrians and light trucks and crashes occurring in rural areas, at intersections, during winter, and on weekdays could significantly increase the risk of fatalities. These findings are expected to provide new perspectives for improving EV safety within the wave of automotive electrification. Full article

In the context of global energy conservation and emission reduction, electric vehicles (EVs) are essential for low-carbon transport. However, their rapid growth challenges power grids with load imbalances across networks and increases user charging costs. To address the issues of load balancing across large-scale distribution networks and the charging costs for users, this paper proposes an optimization strategy for EV charging behavior based on deep reinforcement learning (DRL). The strategy aims to minimize user charging costs while achieving load balancing across distribution networks. Specifically, the strategy divides the charging process into two stages: charging station selection and in-station charging scheduling. In the first stage, a Load Balancing Matching Strategy (LBMS) is employed to assist users in selecting a charging station. In the second stage, we use the DRL algorithm. In the DRL algorithm, we design a novel reward function that enables charging stations to meet user charging demands while minimizing user charging costs and reducing the load gap among distribution networks. Case study results demonstrate the effectiveness of the proposed strategy in a multi-distribution network environment. Moreover, even when faced with varying levels of EV user participation, the strategy continues to demonstrate strong performance. Full article

The drive torque of each wheel hub motor of a four-wheel independent wire-controlled steering electric vehicle is independently controllable, representing a typical over-actuated system. Through optimizing the distribution of the drive torque of each wheel, fault-tolerant control can be realized. In this paper, the four-wheel independent wire-controlled steering electric vehicle is taken as the research object, aiming at the collaborative control problem of trajectory tracking and yaw stability when the actuator of the by-wire steering system fails, a fault-tolerant control method based on the synergy of differential steering and direct yaw moment is proposed. This approach adopts a hierarchical control system. The front wheel controller predicts the necessary steering angle in accordance with a linear model and addresses the requirements of the front wheels and additional torque. Subsequently, considering the uncertainties in the drive control system and the complexities of the road obstacle model, the differential steering torque is computed via the sliding mode control method; the lower-level controller implements the torque optimization distribution strategy based on the quadratic programming algorithm. Finally, the validity of this approach under multiple working conditions was verified via CarSim 2019 and MATLAB R2023b/Simulink simulation experiments. Full article

Due to detection errors, motor parameter deviations, and other uncertainties, traditional motor flux estimation models suffer from the complications of DC bias and high-order harmonics. To address these issues, two flux observers, the second-order generalized integrator flux observer (SOIFO) and the second-order SOIFO, are designed for position sensorless control of permanent magnet synchronous motors (PMSMs). The position sensorless control of PMSMs based on an improved second-order SOIFO is proposed in this paper. The proposed method enhances the frequency-locked loop (FLL) in the observer by introducing a double-axis frequency-locked loop (DFLL), which improves the dynamic performance and disturbance rejection capability of the flux observer. By replacing FLL with DFLL for angular frequency estimation, the method effectively eliminates second-harmonic interference while reducing estimation delays, leading to faster and more accurate rotor position estimation in the second-order SOIFO. Additionally, the improved observer demonstrates enhanced robustness against disturbances, ensuring more stable position sensorless control. The effectiveness of the proposed approach is validated through both simulations and experimental comparisons. Full article

Climate change is one of the problems that affects the climate, natural disasters, and lives, economies, and industries around the world. Since the main cause is the combustion of fossil fuels, the transportation sector is a significant factor in causing these problems. Therefore, many countries, including Thailand, have policies to promote the increased use of electric vehicles. However, past measures have focused mostly on promoting the use of personal electric vehicles. For public transportation, buses are a major part of creating pollution and the problems of particulate matter with a diameter of less than 2.5-micron (PM 2.5), which is another major problem in Thailand because Thailand has many old buses. However, pushing transport operators to switch from internal combustion engine (ICE) buses to electric buses requires a large budget. Therefore, the conversion of old ICE buses into electric buses is one approach that can help promote the use of electric buses to become more possible. Another issue that makes transport operators afraid to switch from ICE buses to electric buses is the shortage of maintenance personnel. Therefore, this action research focuses on creating knowledge and practical skills related to electric vehicle modification and maintenance in the education sector. From the results of this practical research, the researcher was able to modify the old ICE bus into an electric bus and passed the test according to the research objectives. Full article

Developing nations like Bangladesh have yet to adopt hybrid (HEVs) or electric vehicles (EVs) for goods carrying, whereas environmental pollution and fuel costs are hitting hard. The electrically powered cars and trucks market promises an excellent opportunity for environmentally friendly transportation. However, these countries’ inadequate infrastructure, substantial initial expenses, and insufficient policies impeding widespread acceptance hold market growth back. This study examines the current status of the electric car market in low- and middle-income developing nations like Bangladesh, focusing on the infrastructure and regulatory framework-related barriers and the aspects of growth promotion. To promote an expanding hybrid and EV ecosystem, this article outlines recent studies and identifies critical regions where support for policy and infrastructural developments is needed. It discusses how developing nations may adapt successful international practices to suit their specific needs. At the same time, the research adopted system dynamics and case study methods to assess the transportation fleet (142 vehicles) of a livestock farm and find the feasibility of adopting HEVs and EVs. Several instances are improving infrastructures for recharging, providing incentives for lowering the adoption process cost, and creating appropriate regulatory structures that promote corporate and consumer involvement. Findings highlight how crucial it is for governments, businesses, customers, and international bodies to collaborate to build an affordable and sustainable EV network. The investigation concludes with recommendations for more research and appropriate regulations that may accelerate the adoption of EVs, reduce their adverse impacts on the environment, and promote economic growth. Full article

This article presents the instrumentation of an electronic–mechanical differential prototype, consisting of an arrangement of three throttles to operate two hub motors on the rear wheels of an electric vehicle. Each motor is connected to its respective throttle, while a third throttle is connected in series with the other two. This configuration allows for speed control during both rectilinear and curvilinear motion, following Ackermann differential geometry, in a simple manner and without the need for complex electronic systems that make the electronic differential more expensive. The differential throttles are strategically positioned on the mass bars connected to the steering system, ensuring that the rear wheels maintain the appropriate differential ratio. For this reason, it is referred to as an “electronic–mechanical differential”. Additionally, this method can be extended to a four-wheel differential system. Full article

In addition to the widely recognized benefits of reducing carbon emissions and protecting the environment, the authors believe that bus electrification has potential advantages in enhancing driving safety, improving passenger comfort, and reducing driver fatigue—areas that have not yet been sufficiently studied and emphasized. Safety and comfort are fundamental objectives in the continuous development of transportation systems. They are directly and closely related to both passengers and drivers and are among the top priorities when individuals choose their mode of transportation. Therefore, these aspects deserve broader and more in-depth attention and research. This study aims to identify the potential advantages of route bus electrification in terms of safety and comfort. The results of a passive experiment on the speed profile of buses operating on actual routes are presented here. Firstly, we focus on the acceleration/deceleration at the starting/stopping stops, specifically for regular-route buses, and obtain the following information: I. Starting acceleration from a bus stop is particularly strong in the second half of the acceleration process, being suitable for motor-driven vehicles. II. The features of the stopping deceleration at a bus stop are “high intensity” and “low dispersion”, with the latter enabling the refinement of regenerative settings and significantly lowering electricity economy during electrification. And we compare the speed profile of an electric bus with those of a diesel bus and obtain the following information: III. Motor-driven vehicles offer the advantages of “high acceleration performance” and “no gear shifting”, making them particularly suitable for the high-intensity acceleration required when route buses depart from stations. This not only simplifies driving operations but also enhances lane-changing safety. And by calculating and analyzing the jerk amount, we could quantitatively demonstrate the comfortable driving experience while riding on this type of bus where there is no shock due to gear shifting. IV. While the “high acceleration performance” of motor-driven vehicles produces “individual differences in the speed change patterns”, this does not translate to “individual differences in electricity consumption”, owing to the characteristics of this type of vehicle. With engine-driven vehicles, measures such as “slow acceleration” and “shift up early” are strongly encouraged to realize eco-driving, and any driving style that deviates from these measures is avoided. However, with motor-driven vehicles, the driver does not need to be too concerned about the speed change patterns during acceleration. This characteristic also suggests a benefit in terms of the electrification of buses. Full article

A hybrid mining dump truck contains multiple power sources and has a variety of operating modes. When the vehicle switches between different operating modes, inappropriate control strategies will result in insufficient power or excessive output torque ripple. The resulting vehicle shock and the dynamic characteristics of the dynamic clutch mechanism will affect ride comfort. In order to improve the performance of the hybrid mining dump truck in the mode switching process as much as possible, this paper takes power-split hybrid special transmission as the research object and proposes a hybrid model predictive control (HMPC) strategy. However, the simulation time of the HMPC algorithm is about 27% less than that of the MPC algorithm. HMPC can significantly shorten the control time while improving the vehicle ride comfort, reducing the sliding friction work during mode switching, and improving the real-time and robustness of mode switching. Full article

The rapid growth of electric vehicle (EV) adoption has led to an increased demand for charging infrastructure, creating significant challenges for power grid load management and dispatch optimization. This paper addresses these challenges by proposing a coordinated optimization dispatch strategy for EV charging, which integrates time, space, and varying power requirements. This study develops a dynamic spatiotemporal distribution model that accounts for charging demand at different power levels, traffic network characteristics, and congestion factors, providing a more accurate simulation of charging demand in dynamic traffic conditions. A comprehensive optimization framework is introduced, and is designed to reduce peak congestion, enhance service efficiency, and optimize system performance. This framework dynamically adjusts the selection of charging stations (CSs), charging times, and charging types, with a focus on improving user satisfaction, balancing the grid load, and minimizing electricity purchase costs. To solve the optimization model, a hybrid approach combining particle swarm optimization (PSO) and the TOPSIS method is employed. PSO optimizes the overall objective function, while the TOPSIS method evaluates user satisfaction. The results highlight the effectiveness of the proposed strategy in improving system performance and providing a balanced, efficient EV charging solution. Full article

To enhance the driving experience for drivers, making it more realistic and comfortable, this study proposes a simulation control strategy for steering-by-wire road sense based on sliding mode control. Firstly, a dynamic model is constructed for the steering wheel module and the steering actuator, building upon this, a methodology for calculating the self-aligning torque is formulated, utilizing the linear two-degree-of-freedom vehicle model in conjunction with the Magic Formula. The approach incorporates the influences of assistive forces, damping, and frictional torque, thereby providing an accurate simulation of the steering road feel. On this basis, a sliding mode control algorithm is developed to achieve the necessary road-sensing motor current, guaranteeing system stability and rapid response across various driving scenarios. To validate the simulation, MATLAB/Simulink in conjunction with Carsim is employed. The outcomes from tests involving dual lane shifts and step inputs demonstrate that the introduced control approach effectively follows the target current, featuring swift convergence and minimal response latency. Full article

This study examined the relationship between service quality, trust, and passenger satisfaction in sustainable e-public transportation, using the Doha Metro in Qatar as a case study. Despite its advanced automation, the metro faces low adoption, with less than 30% of the economically active population utilizing it. To address this, this study integrated the Social Exchange Theory (SET) and the SERVQUAL RATER model with machine learning techniques to assess commuter perceptions and satisfaction. Neural network and Long Short-Term Memory (LSTM) models outperformed traditional statistical methods, offering enhanced predictive accuracy. Based on the 319 survey responses, key service quality factors were identified, emphasizing customer experience (NI = 100.0%), security (NI = 99.9%), and service reliability (NI = 90.8%). Findings suggested that improving affordability and dynamic pricing could increase metro ridership while reducing private vehicle reliance. Additionally, predictive maintenance and crisis management strategies are recommended to enhance service reliability. This study contributes to sustainable urban mobility by providing data-driven recommendations for efficient and environmentally friendly e-public transportation. Policymakers and urban planners can utilize these insights to improve commuter satisfaction and transit system adoption. Future research may explore multi-city comparisons and hybrid modeling techniques for further refinement. Full article

The rapid development of autonomous driving has underscored the pivotal role of 3D perception. Monocular 3D object detection, as a cost-effective alternative to expensive lidar systems, is garnering increasing attention. However, existing pseudo-lidar methods encounter challenges such as coarse quality and insufficient semantic information when generating 3D point clouds from monocular images. To address these issues, this paper introduces MSFNet3D, which aims to overcome the quality limitations of pseudo-lidar point cloud. Our contributions are threefold: (1) We introduce a dual-branch network to optimize depth maps and propose a multi-scale channel spatial attention module (MS_CBAM). This module captures multi-scale geometric features through a hierarchical feature pyramid and an adaptive weight allocation mechanism, thereby addressing the scale sensitivity inherent in traditional attention mechanisms. (2) We propose a consistency-weighted fusion strategy that employs local gradient consistency analysis and differentiable weighted optimization to achieve a pixel-level fusion of image and depth features. This approach reduces feature conflicts within the dual-branch network and enhances the model’s robustness in complex scenes. (3) We introduce a semantic-guided pseudo-point cloud enhancement method that leverages an instance segmentation network to extract object-specific semantic regions and generate high-confidence point cloud, consequently improving the accuracy of object detection. Experiments on the KITTI dataset show that the proposed method performs excellently under various detection challenges, achieving an average precision of 18.87% in the 3D detection of car objects, which is a 1.67% improvement over the original model. The method also shows good performance in detecting pedestrians and cyclists. The proposed framework can provide economical and reliable 3D perception for mass-produced electric vehicles. Full article

Precise and real-time estimation of vehicle mass and road slope plays a pivotal role in attaining accurate vehicle control. Currently, road slope estimation predominantly emphasizes longitudinal slopes, with limited research on intricate slopes that include both longitudinal roads and continuous turning up-and-down slopes. To address the limitations in existing road slope estimation research, this paper puts forward a novel joint-estimation approach for vehicle mass and road slope. Vehicle mass is initially estimated via M-estimation and recursive least squares with a forgetting factor (FFRLS). A road slope estimate approach, which utilizes interacting multiple models (IMM) and cubature Kalman filtering (CKF), is proposed for complex road slope scenarios. This algorithm integrates kinematic and dynamic vehicle models within the multi-model (MM) ensemble of the IMM filter. The kinematic vehicle model is appropriate for longitudinal road gradients, whereas the dynamic vehicle model is better suited for continuous turning up-and-down slope conditions. The IMM filter employs a stochastic process to weight the appropriate vehicle model according to the driving conditions. Consequently, the weights assigned by the IMM filter enable the algorithm to adaptively select the most suitable vehicle model, leading to more accurate slope estimates under complex conditions compared to single-model-based algorithms. Simulations were carried out using Matlab/Simulink2020-Trucksim2020 to verify the effectiveness of the proposed estimation approach. The results demonstrate that, compared with existing methods, the proposed estimation approach has achieved an improvement in the precision of evaluating vehicle mass and road gradient, thus confirming its superiority. Full article

In low-illumination environments, traditional traffic accident survey methods struggle to obtain high-quality data. This paper proposes a traffic accident reconstruction method utilizing an unmanned aerial vehicle (UAV) and auxiliary equipment. Firstly, a methodological framework for investigating traffic accidents under low-illumination conditions is developed. Accidents are classified based on the presence of obstructions, and corresponding investigation strategies are formulated. As for the unobstructed scene, a UAV-mounted LiDAR scans the accident site to generate a comprehensive point cloud model. In the partially obstructed scene, a ground-based mobile laser scanner complements the areas that are obscured or inaccessible to the UAV-mounted LiDAR. Subsequently, the collected point cloud data are processed with a multiscale voxel iteration method for down-sampling to determine optimal parameters. Then, the improved normal distributions transform (NDT) algorithm and different filtering algorithms are adopted to register the ground and air point clouds, and the optimal combination of algorithms is selected, thus, to reconstruct a high-precision 3D point cloud model of the accident scene. Finally, two nighttime traffic accident scenarios are conducted. DJI Zenmuse L1 UAV LiDAR system and EinScan Pro 2X mobile scanner are selected for survey reconstruction. In both experiments, the proposed method achieved RMSE values of 0.0427 m and 0.0451 m, outperforming traditional aerial photogrammetry-based modeling with RMSE values of 0.0466 m and 0.0581 m. The results demonstrate that this method can efficiently and accurately investigate low-illumination traffic accident scenes without being affected by obstructions, providing valuable technical support for refined traffic management and accident analysis. Moreover, the challenges and future research directions are discussed. Full article

This research focuses on the development of a chassis dynamometer for light electric vehicles (LEV), utilizing the Prony Brake method for torque measurement. The primary goal was to create a robust testing platform that accurately assesses the performance characteristics of LEVs under controlled conditions. The dynamometer’s performance evaluation revealed an average error of 0.55 for RPM readings, indicating a moderate level of variability in the sensor’s accuracy. In contrast, the torque measurement yielded a significantly lower average error of 0.03, demonstrating high precision in capturing torque data. Additionally, a standard deviation of 0.34 was observed during the torque versus RPM assessments, reflecting the consistency of the collected data. These findings validate the effectiveness of the chassis dynamometer in delivering reliable performance metrics for LEVs, providing essential insights for future advancements in electric vehicle technology and performance evaluation methodologies. Full article

Efficient heat dissipation remains a critical challenge in the research and development of automotive permanent magnet synchronous motors. In this study, a Tesla valve cooling channel is innovatively designed, and a corresponding flow model is established using computational fluid dynamics (CFD) simulations. The effects of the spacing between adjacent Tesla valves, the number of stages, and inlet velocities on motor temperature rise and pressure drop within the channel are analyzed under varying flow directions. A comprehensive evaluation of 25 simulation samples reveals that the reverse flow Tesla valve-type channel, with an inlet velocity of 1 m/s, 90 mm spacing, and 16 stages, achieves an optimal balance between cooling performance and energy consumption. Compared to the conventional spiral waterway design, this configuration reduces the maximum temperature and temperature difference by 1.5% and 2.2%, respectively, while maintaining a relatively low pressure drop. Additionally, the structure enhances the coolant’s heat exchange capacity, effectively lowering the peak temperature of the motor. These findings provide valuable insights for advancing motor cooling technologies. Full article

State-of-charge (SOC) estimation is pivotal in optimizing lithium-ion battery management systems (BMSs), ensuring safety, performance, and longevity across various applications. This study introduces a novel SOC estimation framework that uniquely integrates Long Short-Term Memory (LSTM) neural networks with Hyperband-driven hyperparameter optimization, a combination not extensively explored in the literature. A comprehensive experimental dataset is created using data of LG 18650HG2 lithium-ion batteries subjected to diverse operational cycles and thermal conditions. The proposed framework demonstrates superior prediction accuracy, achieving a Mean Square Error (MSE) of 0.0023 and a Mean Absolute Error (MAE) of 0.0043, outperforming traditional estimation methods. The Hyperband optimization algorithm accelerates model training and enhances adaptability to varying operating conditions, making it scalable for diverse battery applications. Developing an intuitive, real-time user interface (UI) tailored for practical deployment bridges the gap between advanced SOC estimation techniques and user accessibility. Detailed residual and regression analyses confirm the proposed solution’s robustness, generalizability, and reliability. This work offers a scalable, accurate, and user-friendly SOC estimation solution for commercial BMSs, with future research aimed at extending the framework to other battery chemistries and hybrid energy systems. Full article

Research on lithium-ion batteries has been driven by the growing demand for electric vehicles to mitigate greenhouse gas emissions. Despite advances, batteries still face significant challenges in efficiency, lifetime, safety, and material optimization. In this context, the objective of this research is to develop a predictive model based on Deep deep-Learning learning techniques. Based on Deep Learning techniques that combine Transformer and Physicsphysics-Informed informed approaches for the optimization and design of electrochemical parameters that improve the performance of lithium batteries. Also, we present a training database consisting of three key components: numerical simulation using the Doyle–Fuller–Newman (DFN) mathematical model, experimentation with a lithium half-cell configured with a zinc oxide anode, and a set of commercial battery discharge curves using electronic monitoring. The results show that the developed Transformer–Physics physics-Informed informed model can effectively integrate deep deep-learning DNF to make predictions of the electrochemical behavior of lithium-ion batteries. The model can estimate the battery battery-charge capacity with an average error of 2.5% concerning the experimental data. In addition, it was observed that the Transformer could explore new electrochemical parameters that allow the evaluation of the behavior of batteries without requiring invasive analysis of their internal structure. This suggests that the Transformer model can assess and optimize lithium-ion battery performance in various applications, which could significantly impact the battery industry and its use in Electric Vehicles vehicles (EVs). Full article

As electric vehicles (EVs) continue to develop, effective battery thermal management systems (BTMSs) are critical for ensuring battery safety, performance, and longevity. This review explores the application of graphene-based materials in BTMSs, focusing on graphene coatings, graphene nanofluids, and enhanced phase change materials (PCMs). Graphene’s superior thermal and electrical conductivities offer substantial benefits for improving heat dissipation, reducing temperature fluctuations, and enhancing battery performance. Despite its potential, challenges such as high production costs and complex manufacturing processes hinder large-scale adoption. This paper summarizes recent advancements and compares graphene’s performance with conventional materials. Key findings, including performance metrics from studies, are discussed to demonstrate the advantages of graphene. The review also outlines future research directions, emphasizing the development of hybrid materials, combining graphene with other advanced substances to optimize EV thermal management. The findings aim to guide future innovations in the field. Full article

The electrification of integrated stereoscopic transportation is a critical step toward achieving sustainable urban mobility and addressing the environmental challenges posed by traditional transportation modes. This study focuses on the case of Jiangsu Province, a leading region in China for electric transportation development, and examines the electrification trends in three key transportation sectors: electric public transit, ride-hailing services, and electric Vertical Takeoff and Landing (eVTOL) systems. The objective of this study is to analyze the current state of these sectors, identify major challenges, and evaluate the effectiveness of existing policies in facilitating this transition. A mixed-methods approach was employed, including bibliometric analysis, keyword clustering, and a detailed review of government reports and academic literature. The findings highlight significant environmental, social, and economic benefits of transportation electrification, while also uncovering barriers such as infrastructure limitations, regulatory gaps, and public acceptance issues. Based on these insights, policy recommendations are proposed to address these challenges and accelerate the adoption of electric transportation systems, contributing to Jiangsu Province’s broader sustainable development goals. Full article

Electric wheel loaders (EWLs) have emerged as a pivotal innovation in the 2020s, representing a transformative shift toward high-efficiency, low-emission construction machinery. Despite their growing technological and environmental significance, a systematic synthesis of advancements in EWL design, energy optimization, and intelligent control remains absent in the literature. To bridge this gap, this review critically evaluates over 140 studies for comparative analysis. Building on the authors’ ongoing research, this paper categorizes EWL architectures and examines breakthroughs in hydraulic systems, drivetrain configurations, and bucket dynamics optimization. A dedicated focus is placed on energy-saving strategies, including advancements in battery technology, fast-charging infrastructure, intelligent torque distribution, and data-driven modeling of shoveling and operational resistance. The analysis reveals that integrating optimal control strategies with machine learning algorithms—such as model predictive control (MPC)—is a critical pathway to achieving energy-efficient and assisted driving in next-generation EWLs. Furthermore, this review advocates for the adoption of distributed electro-hydraulic drive systems to minimize hydraulic losses and enable efficient energy recovery during actuator control. By synthesizing these insights, this work not only highlights current technological frontiers but also proposes actionable research directions to accelerate the commercialization of intelligent, sustainable EWLs. Full article

A coordinated strategy is proposed to prevent interference between trajectory tracking control and stability control in wheel-driven autonomous vehicles. A tire cornering stiffness estimate model is developed using the recursive least squares approach with a forgetting factor (FFRLS), resulting in precise estimation of tire cornering stiffness. An adaptive trajectory tracking control is developed, utilizing real-time updates of tire cornering stiffness; for the direct yaw moment required for stability control, an integral sliding-mode control is adopted, and the chatter problem of the integral sliding-mode controller is optimized by a fuzzy controller. The coordinated control of trajectory tracking and vehicle stability is ultimately attained through the application of the normalized stability index. The method’s practicality is validated by the hardware-in-the-loop simulation platform. Full article

This research presents an intelligent, environment-aware navigation framework for smart electric vehicles (EVs), focusing on multi-agent mapping, real-time obstacle recognition, and adaptive route optimization. Unlike traditional navigation systems that primarily minimize cost and distance, this research emphasizes how EVs perceive, map, and interact with their surroundings. Using a distributed mapping approach, multiple EVs collaboratively construct a topological representation of their environment, enhancing spatial awareness and adaptive path planning. Neural Radiance Fields (NeRFs) and machine learning models are employed to improve situational awareness, reduce positional tracking errors, and increase mapping accuracy by integrating real-time traffic conditions, battery levels, and environmental constraints. The system intelligently balances delivery speed and energy efficiency by dynamically adjusting routes based on urgency, congestion, and battery constraints. When rapid deliveries are required, the algorithm prioritizes faster routes, whereas, for flexible schedules, it optimizes energy conservation. This dynamic decision making ensures optimal fleet performance by minimizing energy waste and reducing emissions. The framework further enhances sustainability by integrating an adaptive optimization model that continuously refines EV paths in response to real-time changes in traffic flow and charging station availability. By seamlessly combining real-time route adaptation with energy-efficient decision making, the proposed system supports scalable and sustainable EV fleet operations. The ability to dynamically optimize travel paths ensures minimal energy consumption while maintaining high operational efficiency. Experimental validation confirms that this approach not only improves EV navigation and obstacle avoidance but also significantly contributes to reducing emissions and enhancing the long-term viability of smart EV fleets in rapidly changing environments. Full article

To improve the path tracking performance of intelligent hybrid articulated tractors in all working conditions in unmanned operation, a path-tracking control method based on corrected model predictive control is proposed. The kinematic model of the tractor is established by analyzing the tractor’s kinematics. Taking the lateral and longitudinal errors as the target and the speed and articulation angular acceleration as the constraints, a tracking control algorithm based on model predictive control is proposed. In addition, to improve the transient performance of the tractor in the path tracking process, the proportional-integral-derivative controller and fuzzy controller are used to correct the model-predicted output articulation angular acceleration, forming a corrected model predictive control path tracking control method. To verify the effectiveness of the control method, model predictive control is used as a comparison method, and the effectiveness of the proposed method is verified based on the MATLAB 2024a simulation platform. The results show that compared with the MPC algorithm, the speed standard deviation is reduced by 2%, the longitudinal tracking error is reduced by 8%, and the lateral tracking error is reduced by 50%. The proposed method can effectively improve the path-tracking accuracy of the intelligent hybrid tractor. Full article

In the pursuit of sustainable urban transportation, electric buses (EBs) have emerged as a promising solution to reduce emissions. The increasing adoption of EBs highlights the critical need for accurate energy consumption prediction. This study presents a comprehensive methodology integrating traction modeling with a Light Gradient Boosting Machine (LightGBM)-based trip-level energy consumption prediction framework to address challenges in power system efficiency and passenger load estimation. The proposed approach combines transmission system efficiency evaluation with dynamic passenger load estimation, incorporating temporal, weather, and driving pattern features. The LightGBM model, hyperparameter tuned through Bayesian Optimization (BO), achieved a mean absolute percentage error (MAPE) of 3.92% and root mean square error (RMSE) of 1.398 kWh, outperforming traditional methods. SHAP analysis revealed crucial feature impacts on trip-level energy consumption predictions, providing valuable insights for operational optimization. The model’s computational efficiency makes it suitable for real-time IoT applications while establishing precise parameters for future optimization strategies, contributing to more sustainable urban transit systems. Full article

To address the need for an in-depth exploration of traffic accidents involving intelligent vehicles and to elucidate the influence mechanism of road environment interference factors on both assisted driving systems and human drivers during such accidents, a comprehensive analysis has been conducted using the System-Theoretic Process Analysis (STPA) framework. This analysis focuses on road static facilities, traffic dynamic characteristics, and instantaneous weather conditions in automobile traffic accidents that occur under the human-machine co-driving paradigm with integrated assisted driving functions. Based on these insights, an interference model tailored to road environment factors in traffic accidents of assisted driving vehicles has been constructed.Utilizing recent traffic accident cases in China, the Accident Map (AcciMap) methodology was employed to systematically classify and analyze all accident participants across six levels. Through this rigorous process, 59 accident factors were refined and optimized, culminating in a method for assessing the degree of interference posed by road environment factors in traffic accidents involving assisted driving vehicles. The ultimate objective of this research is to enhance the investigation of road environment interference factors following accidents that occur with diverse assisted driving functions in human-machine co-driving scenarios. By providing a structured and analytical approach, this study aims to support future research endeavors in developing effective traffic accident prevention countermeasures tailored to assisted driving vehicles. Full article