World Electric Vehicle Journal

Due to global warming, ozone depletion and their ramifications on the Arctic and Antarctic snowscapes, there has been an incentivized drive towards net zero-carbon emission policies by several countries. These policies extend to several sectors, including several manufacturing and processing industries and transportation, which are a few of their notable stakeholders. In the transportation sector, this journey towards net zero-carbon emissions is aided by the adoption of battery electric vehicles (BEVs) due to their zero-carbon emissions during operation. However, they might have zero running emissions, but they do have emissions when charging through conventional sources. This research paper looks at the carbon emissions produced by both electric vehicles (EVs) and internal combustion engine (ICE) vehicles during their operational stages and compares them based on a 200,000 km driving range, battery manufacturing emissions and different power production alternatives to draw up some very important recommendations. The analysis presented in this paper helps in drawing conclusions and proposes ideas which, when included in transport policies, will help curb global warming and eventually lead to the sustainable development of the transport sector. The analysis in this study shows that the emissions needed to produce a single battery unit have increased by approximately 258.7% with the change in battery production locations. Furthermore, charging EVs with a fossil-fuel-dominated grid has shown an increase in emissions of 17.98% compared to the least emissive ICE car considered in the study. Finally, policy update recommendations which are essential for the sustainable development of the transport sector are discussed. Full article

With the widespread adoption of electric vehicles (EVs), optimizing their charging and discharging strategies to improve energy efficiency and extend battery life has become a focal point of current research. Traditional charging and discharging strategies often fail to adequately consider the battery’s state of health (SOH), resulting in accelerated battery aging and decreased efficiency. In response, this paper proposes a safe reinforcement learning–based optimization method for EV charging and discharging strategies, aimed at minimizing charging and discharging costs while accounting for battery SOH. First, a novel battery health status prediction model based on physics-informed hybrid neural networks (PHNN) is designed. Then, the EV charging and discharging decision-making problem, considering battery health status, is formulated as a constrained Markov decision process, and an interior-point policy optimization (IPO) algorithm based on long short-term memory (LSTM) neural networks is proposed to solve it. The algorithm filters out strategies that violate constraints by introducing a logarithmic barrier function. Finally, the experimental results demonstrate that the proposed method significantly enhances battery life while maintaining maximum economic benefits during the EV charging and discharging process. This research provides a novel solution for intelligent and personalized charging strategies for EVs, which is of great significance for promoting the sustainable development of new energy vehicles. Full article

This study presents a practical methodology for systematically incorporating customer expectations and needs into the market implementation of electric vehicles (EVs). Utilising Quality Function Deployment (QFD), companies can evaluate and understand customer requirements, optimise product improvements, and allocate resources efficiently. Though not widely adopted in many Western contexts, QFD proves valuable in enhancing strategic decision making and improving market penetration. Moreover, the integration of EVs with renewable energy and advancements in battery and grid technologies strengthens their environmental and economic benefits. As technological progress and policy support continue, EVs are positioned to drive sustainable transportation and contribute to global carbon reduction goals. Full article

In intelligent cluster systems, the spatio-temporal complexity of agent data collection and resource allocation, as well as the problems in collaborative organizations, present substantial challenges to efficient resource distribution. To address this, a novel self-organizing prediction method for spatio-temporal resource allocation is proposed. In the spatio-temporal modeling part, dilated convolution is applied for time modeling. Its dilation rate grows exponentially with the layer depth, allowing it to effectively capture the time trends of graph nodes and handle long time series data. For spatial modeling, an innovative dual-view dynamic graph convolutional network architecture is utilized to accurately explore the static and dynamic correlation information of the spatial layout of charging piles. Meanwhile, a composite self-organizing mechanism integrating a trust model is put forward. The trust model assists agents in choosing partners, and the Q-learning algorithm of the intelligent cluster realizes the independent evaluation of rewards and the optimization of relationship adaptation. In the experimental scenario of electric vehicle charging, considering charging piles as agents, under the home charging mode, the self-organizing charging scheduling can reduce the total load range by up to 90.37%. It effectively shifts the load demand from peak periods to valley periods, minimizes the total peak–valley load difference, and significantly improves the security and reliability of the microgrid, thus providing a practical solution for resource allocation in intelligent clusters. Full article

LiFePO₄ batteries are widely used in hybrid electric tractors due to their high energy density, stable working voltage, low self-discharge rate, long cycle life, absence of memory effect, environmental friendliness, and flexible sizing. Accurate State of Charge (SOC) estimation is crucial for Battery Management Systems (BMSs). This study utilizes a LiFePO₄ battery dataset from the University of Maryland to improve SOC estimation accuracy. The forgetting factor recursive least squares method was employed for parameter identification, and a temperature-dependent second-order RC equivalent circuit model was developed in MATLAB R2024a/Simulink. The proposed SAO-LSTM model demonstrated superior SOC estimation performance compared to traditional ampere-hour integration, achieving a 98.23% error reduction. Evaluation results showed 0.39% and 0.31% decreases in root mean square error and mean absolute error, respectively, confirming the model’s robustness and high estimation accuracy for LiFePO₄ batteries in hybrid electric tractors. Full article

We present a reduced-order battery management system (BMS) for lithium-ion cells in electric and hybrid vehicles that couples a physics-based single-particle model (SPM) derived from the Cahn–Hilliard phase-field formulation with a lumped heat-transfer model. A three-dimensional COMSOL^® 5.0 simulation of a LiFePO₄ particle produced voltage and temperature data across ambient temperatures (253–298 K) and discharge rates (1 C–20.5 C). Principal component analysis (PCA) reduced this dataset to five latent variables, which we then mapped to experimental voltage–temperature profiles of an A123 Systems 26650 2.3 Ah cell using a self-normalizing neural network (SNN). The resulting ROM achieves real-time prediction accuracy comparable to detailed models while retaining essential electrothermal dynamics. Full article

Cooperative Unmanned Aerial Vehicle (UAV) technology can significantly improve data acquisition in Internet of Things (IoT) environments, which are characterized by wide distribution and limited capacity of ground-based devices. However, due to the open nature of wireless communications, such applications face security threats posed by UAV authentication, especially in scalable IoT environments. To address such challenges, we propose a lightweight chain authentication protocol for scalable IoT environments (LCAP-SIoT), which uses Physical Unclonable Functions (PUFs) and distributed authentication to secure communications, and a secure data collection algorithm, named LS-QMIX, which fuses the LCAP-SIoT and Q-learning Mixer (QMIX) algorithm to optimize the path planning and cooperation efficiency of the multi-UAV system. According to simulation analysis, LCAP-SIoT outperforms existing solutions in terms of computing and communication costs, and LS-QMIX results in superior performance in terms of data collection rate, task completion time, and the success rate of authentication for newly joined UAVs, indicating the feasibility of LS-QMIX in dynamic expansion scenarios. Full article

In response to the evolving transportation landscape, the safety and durability of hybrid electric vehicles (HEVs) necessitate the development of high-performance, reliable health management systems for batteries. The state of health (SOH) provides vital insights about the performance and longevity of batteries, thus enhancing opportunities for efficient energy management in hybrid systems. Despite various research efforts for battery SOH estimation, many of them fall short of the demands for real-time automotive applications. Real-time SOH estimation is crucial for accurate battery fault diagnosis and maintaining precise estimation of the state of charge (SOC) and state of power (SOP), which are essential for the optimal functioning of hybrid systems. In this study, a fuzzy logic estimation method is deployed to determine the tire road friction coefficient (TRFC) and various control strategies are adopted to establish regenerative cut-off points. A MATLAB-based SOH estimation model was developed using a Kalman SOH estimator, which helps to observe the effects of different control strategies on the battery’s SOH. This approach enhances the accuracy and reliability of SOH estimation in real-time applications and improves the effectiveness of battery fault diagnosis. From the results, ANFIS outperformed standard methods, showing approximately 4–6% higher SOH retention across various driving cycles. Full article

This study presents a Model Predictive Control (MPC) strategy for the Battery Thermal Management System (BTMS) in electric vehicles (EVs) to optimize energy efficiency while maintaining battery temperature within the optimal range. Due to the complexity of BTMS dynamics, a high-fidelity model was developed using MATLAB/Simscape (2021a), and an artificial neural network (ANN)-based model was designed to achieve high accuracy with reduced computational load. To mitigate oscillatory control inputs observed in conventional MPC, an infinity-horizon MPC framework was introduced, incorporating a value function that accounts for system behavior beyond the prediction horizon. The proposed controller was evaluated using a simulation environment against a conventional rule-based controller under varying ambient temperatures. Results demonstrated significant energy savings, including a 78.9% reduction in low-temperature conditions, a 36% reduction in moderate temperatures, and a 27.8% reduction in high-temperature environments. Additionally, the controller effectively stabilized actuator operation, improving system longevity. These findings highlight the potential of ANN-assisted MPC for enhancing BTMS performance while minimizing energy consumption in EVs. Full article

This paper presents an optimization model for the charging infrastructure of electric vehicles (EV) designed to minimize installation costs, maximize the utilization of photovoltaic energy, reduce dependency on the electrical grid, and optimize charging times. The model utilizes methodologies such as Linear Optimal Power Flow (LOPF) to align EV charging schedules with the availability of renewable energy sources. Key inputs for the model include Photovoltaic (PV) production profiles, EV charging demands, specifications of the chargers, and the availability of grid energy. The framework integrates installation costs, grid energy consumption, and charging duration into a weighted objective function, ensuring energy balance and operational efficiency while adhering to budgetary constraints. Five distinct optimization scenarios are analyzed to evaluate the trade-offs between cost, charging duration, and reliance on various energy sources. The simulation results obtained from Cadi Ayyad University validate the model’s effectiveness in balancing costs, enhancing charging performance, and increasing dependence on solar energy. This approach provides a comprehensive solution for the development of sustainable and cost-effective EV charging infrastructure. Full article

The aging effect weakens the capacity of lithium batteries, seriously affecting the performance of electric vehicles. Developing state-of-health estimation technology for lithium batteries can help to optimize the charging and discharging strategies of electric vehicles. This study investigates the use of partial discharge data for SOH estimation to address the unstable output of traditional estimation models when using partial discharge data under low-voltage conditions. This study first used the DoppelGANger network to generate artificially synthesized data. After the data augmentation process, we trained the temporal convolutional network to construct a data-driven SOH model. Finally, the performance of the SOH model output was evaluated using three indicators: RMSE, MAPE, and delta. The proposed method improved five kinds of low-voltage operating conditions in seven testing scenarios compared with traditional SOH estimation models. The experimental results provide a practical solution for data-driven SOH estimation. Full article

Riding comfort and safety are essential requirements for any form of transportation but particularly for electric bicycles (e-bikes), which are highly affected by varying road conditions. These factors largely depend on the effectiveness of the e-bike’s control strategy. While several studies have proposed control approaches that address comfort and safety, vibration—an influential factor in both structural integrity and rider experience—has received limited attention during the design phase. Moreover, many commercially available e-bikes provide manual assistance-level settings, leaving comfort and safety management to the rider’s experience. This study proposes a Road-Adaptive Vibration Reduction System (RAVRS) that can be deployed on an e-bike rider’s smartphone to automatically maintain riding comfort and safety using manual assistance control. A fuzzy-based control algorithm is adopted to dynamically select the appropriate assistance level, aiming to minimize vibration while maintaining velocity and acceleration within thresholds associated with comfort and safety. This study presents a vibration analysis to highlight the significance of vibration control in improving electronic reliability, reducing mechanical fatigue, and enhancing user experience. A functional prototype of the RAVRS was implemented and evaluated using real-world data collected from experimental trips. The simulation results demonstrate that the proposed system achieves effective control of speed and acceleration, with success rates of 83.97% and 99.79%, respectively, outperforming existing control strategies. In addition, the proposed RAVRS significantly enhances the riding experience by improving both comfort and safety. Full article

To optimize the modulated vibration generated by the integer-slot interior permanent magnet synchronous motor (IPMSM), a stepwise segmented skewed pole method was proposed, using an 8-pole 48-slot IPMSM as an example. First, the vibration characteristics of the motor were studied, and the theoretical mechanisms of the magnetic field modulation effect and radial force modulation effect were explained. The study showed that high-order radial forces can excite larger low-order vibrations under the influence of radial force modulation. Then, in response to the axial spacing in the linear skewed pole structure when canceling the 48th-order radial force, a stepwise skewed pole structure was proposed. The suppression mechanism of this skewed pole structure on the motor’s modulated vibration was analyzed, and the optimization effect of different segment numbers on the motor’s vibration acceleration at $12f_e$ was discussed. Finally, models for the motor’s magnetic field, structural field, and acoustic field before and after skewing were established, and simulations were conducted to compare the magnitudes of the radial forces at each order and their vibration noise performance. The results showed that after stepwise skewed pole optimization, the radial force that excites the modulated vibration was reduced by 68%, the maximum vibration acceleration on the casing surface was reduced by 84%, and the overall noise was reduced by 7.491 dB, effectively suppressing electromagnetic vibration noise. Full article

In order to solve the problems of traditional RRT algorithms that are too random in planning, have low planning efficiency, and have insufficient security, this paper proposes an algorithm that fuses APF and the improved bidirectional RRT* algorithm and proposes a heuristic planning strategy to sort multiple target points so that the fusion improvement algorithm can traverse multiple target points with a short path length. This study also aims to improve the RRT* algorithm by using optimization strategies such as bidirectional sampling and adding an adaptive target bias strategy to improve its efficiency in obtaining global paths. The obstacle expansion strategy is used in the APF algorithm to expand the repulsion effect, and the APF algorithm after the obstacle expansion is fused with the improved bidirectional RRT* algorithm, adding gravitational potential field-guided sampling at random points, avoiding local optimum solution, and improving the sampling efficiency while accelerating the acquisition of global paths. The redundant node deletion strategy is introduced to simplify the path, and the repulsive potential field is used to improve the Bezier smoothing method, avoiding collisions caused by path distortion due to smoothing. A multi-target point heuristic planning strategy is proposed to achieve shorter global paths while maintaining shorter local paths, taking into account both local solutions and optimal solutions, so that the fusion algorithm can be applied to the path planning of multi-target points. Full article

The complexity and shortening of development cycles in the automotive industry, particularly with the rise in hybrid electric vehicle sales, increases the need for efficient calibration and testing methods. Virtualization using hardware-in-the-loop testbenches has the potential to counteract these trends, specifically for the calibration of hybrid operating strategies. This paper presents a dynamic closed-loop validation of a hardware-in-the-loop testbench designed for the virtual calibration of hybrid operating strategies for a plug-in hybrid electric vehicle. Requirements regarding the hardware-in-the-loop testbench accuracy are defined based on the investigated use case. From this, a dedicated hardware-in-the-loop testbench setup is derived, including an electrical setup as well as a plant simulation model. The model is then operated in a closed loop with a series production hybrid control unit. The closed-loop validation results demonstrate that the chassis simulation reproduces driving resistance closely aligning with the reference data. The driver model follows target speed profiles within acceptable limits, achieving an R² = 0.9993, comparable to the R² reached by trained human drivers. The transmission model replicates the gear ratios, maintaining rotational speed deviations below 30 min⁻¹. Furthermore, the shift strategy is implemented in a virtual control unit, resulting in a gear selection comparable to reference measurements. The energy flow simulation in the complete powertrain achieves high accuracy. Deviations in the high-voltage battery state of charge remain below 50 Wh in a WLTC charge-sustaining drive cycle and are thus within the acceptable error margin. The net energy change criterion is satisfied with the hardware-in-the-loop testbench, achieving a net energy change of 0.202%, closely matching the reference measurement of 0.159%. Maximum deviations in cumulative high-voltage battery energy are proven to be below 10% in both the charging and discharging directions. Fuel consumption and CO₂ emissions are modeled with deviations below 3%, validating the simulation’s representation of vehicle efficiency. Real-time capability is achieved under all investigated operating conditions and test scenarios. The testbench achieves a real-time factor of at least 1.104, ensuring execution within the hard real-time criterion. In conclusion, the closed-loop validation confirms that the developed hardware-in-the-loop testbench satisfies all predefined requirements, accurately simulating the behavior of the reference vehicle. Full article

This article presents an alternative variant of motion planning techniques for autonomous vehicles (AVs) centered on an inverse approach that concurrently optimizes both trajectory and speed. This method emphasizes searching for a trajectory and distributing its speed within a single road segment, regarded as a final element. The references for the road lanes are represented by splines that interpolate the path length, derivative, and curvature using Cartesian coordinates. This approach enables the determination of parameters at the final node of the road segment while varying the reference length. Instead of directly modeling the trajectory and velocity, the second derivatives of curvature and speed are modeled to ensure the continuity of all kinematic parameters, including jerk, at the nodes. A specialized inverse numerical integration procedure based on Gaussian quadrature has been adapted to reproduce the trajectory, speed, and other key parameters, which can be referenced during the motion tracking phase. The method emphasizes incorporating kinematic, dynamic, and physical restrictions into a set of nonlinear constraints that are part of the optimization procedure based on sequential quadratic optimization. The objective function allows for variation in multiple parameters, such as speed, longitudinal and lateral jerks, final time, final angular position, final lateral offset, and distances to obstacles. Additionally, several motion planning variants are calculated simultaneously based on the current vehicle position and the number of lanes available. Graphs depicting trajectories, speeds, accelerations, jerks, and other relevant parameters are presented based on the simulation results. Finally, this article evaluates the efficiency, speed, and quality of the predictions generated by the proposed method. The main quantitative assessment of the results may be associated with computing performance, which corresponds to time costs of 0.5–2.4 s for an average power notebook, depending on optimization settings, desired accuracy, and initial conditions. Full article

To address the performance degradation in model predictive control (MPC) under vehicle state uncertainties caused by external disturbances (e.g., crosswinds and tire cornering stiffness variations) and rigid constraint conflicts, we propose a robust MPC framework with adaptive weight adjustment and dynamic constraint relaxation. Traditional MPC methods often suffer from infeasibility or deteriorated tracking accuracies when handling model mismatches and disturbances. To overcome these limitations, three key innovations are introduced: a three-degree-of-freedom vehicle dynamic model integrated with recursive least squares-based online estimation of tire slip stiffness for real-time lateral force compensation; an adaptive weight adjustment mechanism that dynamically balances control energy consumption and tracking accuracy by tuning cost function weights based on real-time state errors; and a dynamic constraint relaxation strategy using slack variables with variable penalty terms to resolve infeasibility while suppressing excessive constraint violations. The proposed method is validated via ROS (noetic)–MATLAB2023 co-simulations under crosswind disturbances (0–3 m/s) and varying road conditions. The results show that the improved algorithm achieves a 13% faster response time (5.2 s vs. 6 s control cycles), a 15% higher minimum speed during cornering (2.98 m/s vs. 2.51 m/s), a 32% narrower lateral velocity fluctuation range ([−0.11, 0.22] m/s vs. [−0.19, 0.22] m/s), and reduced yaw rate oscillations ([−1.8, 2.8] rad/s vs. [−2.8, 2.5] rad/s) compared with a traditional fixed-weight MPC algorithm. These improvements lead to significant enhancements in trajectory tracking accuracy, dynamic response, and disturbance rejection, ensuring both safety and efficiency in autonomous vehicle control under complex uncertainties. The framework provides a practical solution for real-time applications in intelligent transportation systems. Full article

With the rapid development of electric vehicles, the safety and reliability of lithium-ion batteries (LIBs), as their core energy storage units, have become increasingly prominent. The variation in internal battery pressure is closely related to critical issues such as thermal runaway, mechanical deformation, and lifespan degradation. The non-uniform distribution of internal pressure may trigger localized hot spots or even thermal runaway, posing significant threats to vehicle safety. However, traditional external monitoring methods struggle to accurately reflect internal pressure data, and single-point external pressure measurements fail to capture the true internal state of the battery, particularly within battery modules. This limitation hinders efficient battery management. Addressing the application needs of electric vehicle power batteries, this study integrates thin-film pressure sensors into LIBs through the integrated functional electrode (IFE), enabling distributed in situ monitoring of internal pressure during long-term cycling. Compared to non-implanted benchmark batteries, this design does not compromise electrochemical performance. By analyzing the pressure distribution and evolution data during long-term cycling, the study reveals the dynamic patterns of internal pressure changes in LIBs, offering new solutions for safety warnings and performance optimization of electric vehicle power batteries. This research provides an innovative approach for the internal state monitoring of power batteries, significantly enhancing the safety and reliability of electric vehicle battery systems. Full article

This article introduces a novel hybrid database architecture that combines graph and time-series databases to enhance the storage and management of transportation data, particularly for electric vehicles (EVs). This model addresses a critical challenge in modern mobility: handling large-scale, high-velocity, and highly interconnected datasets while maintaining query efficiency and scalability. By comparing a naive graph-only approach with our hybrid solution, we demonstrate a significant reduction in query response times for large data contexts-up to 64% faster in the XL scenario. The scientific contribution of this research lies in its practical implementation of a dual-layer storage framework that aligns with FAIR data principles and real-time mobility needs. Moreover, the hybrid model supports complex analytics, such as EV battery health monitoring, dynamic route optimization, and charging behavior analysis. These capabilities offer a multiplier effect, enabling broader applications across urban mobility systems, fleet management platforms, and energy-aware transport planning. By explicitly considering the interconnected nature of transport and energy data, this work contributes to both carbon emission reduction and smart city efficiency on a global scale. Full article