World Electric Vehicle Journal

As a critical element of the drive motor, rolling bearings are susceptible to localized defects under complex loads and varying operating conditions. Such defects typically generate periodic transient shocks, which reflect bearing fault features. However, the accurate extraction of fault-related transient components becomes challenging due to strong noise influence. To address this issue, a concave sparsity-assisted generalized dispersive mode decomposition (CSA-GDMD) method is developed to enhance fault feature extraction. This method introduces a non-convex sparse model based on generalized mini-max concave (GMC) regularization to preprocess the vibration signal. The GMC penalty effectively suppresses background noise while better preserving the amplitude characteristics of the transient shocks. Subsequently, GDMD is applied to progressively extract transient shock components from the preprocessed signal and reconstruct the signal, resulting in more prominent fault-related transient components. The simulation results show that CSA-GDMD significantly improves the signal-to-noise ratio (SNR), from 6.5905 dB at −15 dB to 9.5122 dB at 5 dB, and reduces the root mean square error (RMSE) from 0.0280 to 0.0196. Consequently, the fault feature frequencies can be identified more clearly in the envelope spectrum, further confirming the accurate fault diagnosis capability of the proposed method for bearing faults under strong noise conditions. Full article

Vehicle-to-Grid (V2G) is increasingly recognized as a promising source of flexibility for low-carbon energy systems, yet its deployment remains limited in practice. While previous research has largely focused on technical feasibility and market integration, less attention has been paid to V2G as a multi-actor service system. This study addresses that gap by applying a service design perspective to the co-development of a V2G service blueprint in the Swedish context. The research was conducted through an exploratory multi-stakeholder co-design process. The resulting blueprint maps customer actions, frontstage and backstage processes, stakeholder interactions, and communication flows across the V2G service lifecycle. The study identifies several service-level challenges related to onboarding, coordination, pre-qualification, contractual complexity, and user-facing value communication. The findings show how service blueprinting can support the structuring, analysis, and early-stage design of V2G services, while also highlighting the need for further validation in pilot implementation and across different regulatory contexts. Full article

This study assesses the impact of regenerative braking on lithium-ion battery aging and operational efficiency of lithium-ion batteries in urban electric buses using a Rainflow-based cycle-counting framework. A previously developed simulation platform based on Energetic Macroscopic Representation (EMR) is employed to reproduce realistic daily driving cycles. Battery degradation is quantified by combining the Rainflow Counting Method with Miner’s Rule, enabling cumulative damage assessment across different depth of discharge (DoD) levels and regenerative braking intensities, k_br. Four representative cycling profiles—fixed 50%, 60%, and 70% DoD and a variable mixed-use scenario—were simulated under regenerative braking intensities ranging from 0% to 100%. Results indicate that regenerative braking extends average battery lifespan by approximately 0.9 years while increasing daily driving range by around 6 km. Profiles with lower DoD values, particularly when combined with moderate regenerative braking (k_br ≈ 0.3), achieved the most favourable balance between cycle induced degradation and energy recovery. Although higher DoD scenarios deliver greater mileage gains, they also accelerate capacity fade. The variable cycling profile demonstrated robust and consistent performance, highlighting the benefits of route and load variability. Additionally, lifetime mileage analysis demonstrates that intermediate DoD levels combined with regenerative braking maximize cumulative energy throughput while preserving service life. Overall, the proposed methodology offers a computationally efficient and practically applicable approach for battery life assessment under dynamic operating conditions, offering valuable insights for optimizing energy management strategies and electric bus fleet operations. Full article

The second-life application of vehicle batteries is getting attention as millions of battery systems, modules, or cells are going to enter the market in the coming decade. The performance uncertainty with or without historical knowledge of the batteries’ vehicle usage is a concern. Moreover, detailed studies on second-life battery cell behavior is sparse and an improved understanding is required for reuse/repurpose. In this work, two second-life battery packs are dismantled, and the extracted prismatic and pouch Nickel–Manganese–Cobalt (NMC) cells with 141 Ah and 65 Ah, respectively, are extensively investigated to understand the second-life degradation behavior. The one-and-a-half-year-long test campaign has followed dedicated suitable stationary test matrices, generating a valuable dataset. The aging dataset is then filtered with the most correlated features via Pearson correlation analysis (PCA) and used to train different machine learning algorithms, resulting in a root-mean-square-error (RMSE) of 0.065 and 0.109 for prismatic and pouch cells, respectively, with the best-performing ElasticNet model validated against real-life stationary profiles. The developed framework is suitable for edge computation where the SoH could be evaluated online, facilitating state-based performance and lifetime extension. Full article

This study develops a multi-criteria and AI-assisted optimization framework that integrates the Analytic Hierarchy Process (AHP), K-means clustering, and Genetic Algorithm (GA) optimization within a Geographic Information System (GIS) environment to optimize electric vehicle (EV) charging station deployment across Abu Dhabi’s urban–rural gradient. The model generates a community-level Spatial Suitability Index (mean = 0.47) based on residential, commercial, and accessibility factors, which inform clustering into five deployment typologies reflecting distinct socio-spatial characteristics. GA-based spatial optimization under two policy pathways, Progressive and Thriving, balances accessibility, grid proximity, and utilization efficiency. Results show that the Thriving scenario achieves approximately 15–20% higher network coverage and equity compared to the Progressive case, demonstrating the value of adaptive, data-driven optimization for mixed urban–rural contexts. The integrated AHP–Clustering–GA approach provides a transferable and scalable blueprint for equitable, low-carbon mobility infrastructure planning in rapidly developing regions. Full article

The SAE J2841 utility factor (UF) estimates the fraction of driving expected to occur in charge-depleting (CD) mode for plug-in hybrid electric vehicles. Emerging in-use data suggest that real-world electric usage is lower than assumed, motivating a reassessment of how charging behavior and related factors should be incorporated into the UF curve. Using trip-level data from approximately 1000 PHEVs observed over one year, we develop a charging model that captures both population-level heterogeneity in charging frequency and day-to-day characteristic temporal patterns in individual charging. The charging behavior modeling is applied to NHTS driving data to generate UF curves spanning 5 to 200 miles (8 to 322 km) of CD range. When key behavioral features are included, the resulting CD driving fractions align closely with industry-provided data. Sensitivity analysis indicates that the assumed share of habitual non-chargers is among the most influential parameters affecting the gap between the original UF and in-use data. Multiple modeling approaches were used to explore the problem and compare results, including machine learning, logistic regression, and parametric methods. Additional factors such as blended CD operation and temperature effects are discussed within a modular framework for refining J2841. These findings inform ongoing discussions on PHEV utility representation in analytical and regulatory contexts. Full article

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators (e.g., SUMO), which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article

Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking internal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle refinement. This review synthesizes the current state of the art in harmonic NVH engineering for electric drivetrains, focusing on the interactions between gear geometry, manufacturing variability, electromechanical coupling, structural transfer, and human sound perception. Classical mechanisms of gear-mesh excitation are revisited together with emerging EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The review further examines recent progress in predictive and data-driven approaches, including machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary system-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review provides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains. Full article

The electrification of regional railway lines using battery-electric trains requires accurate simulation tools to support energy management and thermal control design. This paper presents an integrated dynamic simulation model of the traction system of a Hitachi Caravaggio ETR 521 regional train operating in battery-electric mode, developed in MATLAB/Simulink 2024b. The model incorporates all key drivetrain components, including a train reference generator, speed controller, motor controller, three-phase inverter, induction motor, a Kokam Co., Ltd. lithium-ion battery pack, and a detailed battery thermal management system. The proposed framework enables simultaneous evaluation of traction performance, battery state of charge (SOC) evolution, and thermal behavior under realistic conditions. To validate the model, simulations of the Treviso–Vicenza route were conducted under two scenarios: traction-only operation and operation with a 160 kW auxiliary load. Simulation results demonstrate that auxiliary loads significantly affect energy consumption and battery thermal behavior, with energy consumption increased by 50%. The results highlight the importance of integrating thermal effects into energy management and sizing decisions for battery-electric regional trains. The developed model provides a practical tool for optimizing battery sizing, thermal management strategies, and overall energy performance, supporting the planning and design of sustainable electric railway solutions. The modular MATLAB/Simulink architecture is designed to be route-agnostic; extension to other regional lines with different gradients, speed profiles, or extreme climate conditions (e.g., alpine routes or high-temperature regions) requires only updated route data and adjusted ambient boundary conditions, demonstrating the model’s broad applicability beyond the Treviso–Vicenza case study. Full article

Thermal management is a key factor for the efficiency, performance, and reliability of battery electric vehicles (BEVs), particularly in systems with strongly coupled components and heterogeneous thermal dynamics. This study proposes a centralized nonlinear model predictive control (NMPC) strategy for component cooling in BEVs, designed to maintain temperatures within optimal ranges while minimizing energy consumption and respecting actuator constraints. A reduced-order physics-based model is developed in MATLAB/Simulink R2024b, and the NMPC is implemented using CasADi, incorporating coolant temperatures as stabilizing states and a systematic parametrization of sampling time, prediction horizon, and weighting factors. The considered thermal management system consists of hydraulically coupled subsystems with different overall time constants, for which a single-horizon NMPC formulation is applied. Simulation results show that the proposed controller accurately tracks thermal dynamics across components with varying inertia and effectively captures cross-coupling effects. Sensitivity analyses indicate that variations in sampling time and prediction horizon have a limited impact on temperature trajectories and energy consumption, demonstrating robustness and real-time applicability. Compared to a rule-based controller, the NMPC achieves up to 30% reduction in energy consumption depending on ambient conditions and driving cycles, while improving temperature regulation, particularly for the high-voltage battery, with up to 2 K lower peak temperatures and a more balanced temperature distribution. These findings demonstrate that centralized NMPC is a suitable and efficient approach for thermal management in directly coupled BEV subsystems with heterogeneous dynamics. Full article

A novel facial landmark localization method, termed TSDM, is proposed by integrating T-shaped features with the Supervised Descent Method (SDM). Facial landmark localization is critical for driver fatigue and attention detection in intelligent cockpits. Traditional methods lack accuracy and robustness in complex in-cabin environments such as varying illumination and head pose changes, while deep learning approaches are computationally expensive on resource-constrained vehicle platforms. The T-shaped feature well matches facial geometry and enhances feature representation. T-shaped features are selected via AdaBoost for robust face detection, and SDM is then used to locate 68 facial landmarks. Experiments show that TSDM achieves higher accuracy, lower false-positive rates, and better efficiency than traditional methods, including Haar and LBPH. It also exhibits stronger robustness and better real-time performance than several lightweight deep learning models (such as 3D-aware methods and SAN) on CPU-only platforms, while achieving comparable or higher localization accuracy. Experimental results show that TSDM achieves a face detection rate of 97.43% and a normalized mean error (NME) of 3.4% on standard datasets. The proposed method provides a practical solution for driver state monitoring in resource-limited vehicular environments. Full article

As an important state parameter in battery management systems, accurate state of charge (SOC) estimation is of great significance for the safe and reliable use of batteries. In this paper, a Temporal Convolutional Network–Transformer (TCN–Transformer) model is proposed for achieving accurate estimation of SOC. First, the TCN is integrated in series with the Transformer model. This integration not only extracts the local characteristics of time-series data but also captures broader spatiotemporal correlations, thereby enhancing the feature representation and achieving highly accurate estimation. However, since the hyperparameter settings of neural networks have a significant impact on model performance, this study employs the advanced hippo optimization (HO) algorithm to determine the optimal values for the number of filters, filter size, number of residual blocks, and number of encoder layers, ultimately improving the model’s stability and efficiency. Finally, the proposed model was tested under various dynamic driving conditions at different temperatures. Experimental validation on the CALCE dataset demonstrates that the proposed HO–TCN–Transformer achieves RMSE and MAE both under 0.7%, representing an approximately 50% overall error reduction compared to the standalone TCN. Cross-validation across five folds confirms robust performance with <7% standard deviation. Full article

To address the challenge of balancing regenerative braking efficiency and braking safety in rear-wheel-drive electric motorcycles, this study proposes a genetic algorithm-based electromechanical hybrid fuzzy braking control strategy. First, a three-dimensional fuzzy controller is designed with braking force, motorcycle speed, and battery state of charge (SOC) as input variables to adjust the regenerative braking ratio in real-time. To further improve the fuzzy logic, which typically relies on engineering experience, a genetic algorithm (GA) is employed to optimize the controller’s parameter space. Co-simulation results using BikeSim 2013.1 and MATLAB/Simulink R2022a demonstrate that, under WMTC and NEDC standard driving cycles, the proposed GA-optimized fuzzy control system increases energy recovery rates by 6.59% and 11.65%, respectively, compared with the unoptimized fuzzy control strategy. Full article

Decarbonizing heavy-duty road freight transportation requires efficient energy management in zero-emission powertrains. This study investigates energy management strategies (EMSs) for a heavy-duty Fuel Cell Plug-in Hybrid Electric Vehicle (FC-PHEV). Rather than the typical charge-sustaining operation, these strategies are designed for charge-depleting operation, in which each route begins with a charged battery and ends at a lower state of charge (SOC), leveraging the vehicle’s plug-in capability. The EMSs are evaluated primarily in terms of energy consumption, while battery C-rate and fuel cell ramp rate are used as simple stress indicators for comparative analysis. A backward-facing vehicle model is developed to test several EMSs, including both optimization- and rule-based strategies. The Equivalent Consumption Minimization Strategy (ECMS) emerged as a promising option, motivating further testing with a forward-facing model and additional drive cycles. The simulation results show that ECMS consumed only 1.1% more energy than the global optimal solution found by Pontryagin’s Minimum Principle (PMP) and 7.5% less energy than a simple rule-based strategy, on average across five drive cycles. These results show that ECMS can be effective for a heavy-duty FC-PHEV operating in charge-depleting mode, extending its demonstrated applicability beyond charge-sustaining and light-duty vehicles. Full article

Series-connected lithium-ion battery packs are widely used in electric vehicles (EVs). However, inevitable inconsistency among cells can cause charge imbalance, accelerated aging, and reduced system safety. To improve the consistency of series-connected battery packs under complex EV operating conditions, this study proposes a hierarchical active balancing system. Bidirectional Buck–Boost converters are employed for intra-group balancing, and distributed flyback converters are used for inter-group balancing. A multi-stage coordinated balancing control strategy is further developed to reduce control complexity and improve balancing efficiency. A 16-cell series-connected battery pack model is established in MATLAB R2024a /Simulink and evaluated under resting, charging, and discharging conditions. The results show that, compared with the conventional single-layer Buck–Boost balancing topology, the proposed method reduces the balancing time by 58.09%, 57.97%, and 58.06%, respectively. These results indicate that the proposed system can effectively improve the consistency and balancing performance of series-connected battery packs, providing a scalable solution for EV battery management systems. Full article

To address the issue of vehicle instability and veering during braking when a single wheel fails in an electric vehicle’s electromechanical braking (EMB) system, an integrated application-oriented control framework based on adaptive sliding mode control (ASMC) is proposed. To address the shortcomings of SMC—such as difficulty in suppressing oscillations and the high workload associated with parameter tuning—a novel composite reaching law function was designed, and the TD3 algorithm was employed to optimize the sliding mode control parameters. When a failure in the EMB system is detected, the upper-layer control uses an improved ASMC algorithm to calculate the vehicle’s additional yaw moment. The lower-layer control employs an optimal control algorithm to distribute braking force, taking into account braking intensity, yaw moment, and tire utilization. This approach is integrated with sliding mode steering control to enhance vehicle stability during braking. To meet the driver’s braking requirements, a backpropagation (BP) neural network is first employed to identify braking intent. Based on this, the additional yaw moment is calculated by the upper-layer controller, and the brake force distribution is optimized through the lower-layer controller, thereby improving the vehicle’s stability. Through co-simulation analysis using Simulink-2024a and CarSim-2019.1, the results show that, compared to traditional algorithms, the proposed hierarchical control strategy reduced the maximum sideslip angle by 51.4%, decreased the maximum yaw rate by 47.2%, and reduced the maximum lateral offset by 45.6%. This control strategy enables enhanced stability across various braking intensity conditions. Full article

In recent years, the rapid expansion of electric vehicle (EV) charging infrastructure and the increasing penetration of renewable energy sources require highly efficient and dynamically robust power electronic interfaces. In photovoltaic (PV)-assisted EV charging stations and DC microgrids, bidirectional DC-DC converters (BDCs) are essential for managing power flow between PV arrays, battery energy storage systems, and the DC bus supplying EV chargers. This paper presents a novel voltage and current control design for a BDC operating in a PV-powered DC microgrid oriented to EV charging applications. Following a detailed mathematical model of the converter, a digital current controller and a predictive voltage regulator were developed using Model-Based Predictive Control (MBPC). The proposed cascade control structure enables accurate DC bus voltage regulation and seamless bidirectional power flow under dynamic load variations representative of EV charging and discharging scenarios. The control scheme was evaluated in MATLAB/SIMULINK^® and experimentally validated through Field-Programmable Gate Array (FPGA)-based test benches using an OPAL-RT real-time (RT) simulator, integrating the RT-LAB and RT-eFPGAsim environments. The predictive controller achieved precise regulation in both buck and boost modes, reaching efficiencies of 97.07% and 98.57%, respectively. The results demonstrate that integrating MBPC with RT validation provides high performance, fast dynamic response, and computational efficiency, making the proposed approach suitable for renewable-integrated EV charging stations and next-generation DC microgrid-based mobility systems. Full article