World Electric Vehicle Journal

The output voltage harmonics of electric vehicle (EV) charging converters directly affect grid power quality. This paper proposes a harmonic analysis method based on geometric algebra (GA), which employs a multivector representation of signals and least squares estimation to accurately extract fundamental, integer-order, and inter-harmonics. A coupling coefficient is defined to quantify the phase correlation between frequency components. Based on measured data, harmonic characteristics under four typical operating conditions are analyzed, and an adaptive PID controller is designed to dynamically adjust the virtual resistance for harmonic suppression. The results show that the GA method significantly reduces spectral leakage under non-integer-period sampling conditions, with amplitude estimation errors below ±2%. The total harmonic distortion (THD) decreases with increasing active power and increases with reactive power injection. The droop coefficient exhibits a non-monotonic effect, while reducing the proportional gain raises the THD. Adaptive control reduces the average THD by 14.0–28.5% with a total response time of less than 0.05 s. The coupling coefficient C₁₃ is strongly positively correlated with THD and negatively correlated with the maximum Lyapunov exponent computed using the Rosenstein small-data method (correlation coefficient −0.85), confirming the intrinsic relationship between coupling and stability. Compared with fast Fourier transform (FFT) and other methods, GA achieves higher accuracy under short data records and non-integer-period sampling. This paper provides a complete theoretical framework and engineering solution for harmonic suppression in charging converters. Full article

Considerable progress has been made in predicting nominal NVH behavior in electric drivetrains, but the acoustic scatter observed across manufactured units remains insufficiently understood. In practice, nominally identical drive units may still exhibit noticeably different tonal behavior because small deviations in gears, shafts, bearings, fits, centering features, or assembly phase modify the excitation, transfer, and radiation mechanisms of the system. This review examines how manufacturing and assembly variability influences NVH performance in electric drive units and e-axles, with particular focus on the rotor–shaft–gear–bearing–housing system. Unlike broader EV NVH reviews, the present work focuses specifically on variability-induced acoustic scatter and its propagation along the drivetrain NVH generation and transmission path. To support transparency and consistency, the literature search and selection process followed a structured, PRISMA-inspired approach across Scopus, Web of Science, Google Scholar, and SAE Mobilus for the 2015–2026 period. From 387 identified records, 50 studies were retained after duplicate removal, screening, and full-text assessment. The selected literature was synthesized into eight thematic categories: imbalance; run-out and eccentricity; bearing clearance and preload; spline and pilot centering; thermal effects; phase indexing; transmission error and sidebands; and end-of-line NVH diagnostics. The reviewed literature shows that manufacturing- and assembly-induced deviations can significantly alter transmission error, sideband structure, shaft-order content, and final tonal response, even when individual components remain within nominal tolerance limits. Beyond synthesizing the evidence base, the review organizes existing modeling and diagnostic practices into a structured framework for variability-aware NVH assessment, based on explicit deviation parameterization, hierarchical model fidelity, intermediate excitation metrics, thermal-state awareness, and closer integration with production and measurement data. Overall, the findings support a shift from nominal NVH assessment toward robustness-oriented, production-representative interpretation and future prediction of acoustic scatter in electric drivetrains. Full article

This paper presents a comprehensive methodology for evaluating the flexibility potential of Electric Vehicle (EV) charging infrastructures from the perspective of a Charge Point Operator (CPO). The proposed framework is general and applicable to different types of charging infrastructures, provided that a set of operational assumptions is satisfied. These include unidirectional smart charging (V1G), AC charging sessions, preservation of user energy delivery when providing flexibility, and explicit modeling of rebound effects induced by temporal load shifting, requiring subsequent recovery of the shifted energy. The methodology is then applied to a real-world workplace charging facility to quantify the amount and temporal distribution of flexibility under different baseline charging strategies and levels of on-site photovoltaic integration. The analysis shows that a significant share of daily energy demand (i.e., between 20% and 36%) can be made available for flexibility services within the considered assumptions. Furthermore, the results highlight a strong operating cost trade-off between local optimization strategies and participation in system-level flexibility markets in the considered case study. Full article

With the rapid advancement of technology, robotaxi services have emerged as a pivotal development direction within the transportation industry. Currently, this field is at a critical juncture transitioning from technological R&D to commercial operations, with service coverage continuously expanding and a pressing need to enhance and optimize service quality. This study aims to refine the service quality of robotaxis, elevate user-perceived experiences, and boost satisfaction levels. Through a literature review, we systematically examined the development status and service pain points of robotaxi services both in China and abroad. Leveraging grounded theory, we identified 21 service elements, and designed a bidirectional questionnaire for empirical investigation. Methodological robustness was confirmed through multi-source cross-validation. Then, we classified these elements using the Type-II Candy Model, and prioritized them based on the Average Satisfaction-Dissatisfaction metric. The findings reveal that the service elements of robotaxi services encompass 8 Differentiate factors, 1 Must-be factor, 4 One-dimensional factors, 5 Attractive factors, and 3 Indifferent factors. This study systematically dissects user requirements, enabling enterprises to improve service quality and standards more effectively by aligning with the requirement categories and their priority rankings. It facilitates the construction of a systematic service delivery and evaluation framework, ultimately better meeting consumer requirements. Full article

Achieving simultaneous trajectory accuracy and dynamic stability is challenging for four-wheel independent drive (4WID) electric vehicles under near-limit conditions. To effectively resolve this internal control conflict, this paper proposes a novel normalized stability index that accurately quantifies real-time instability risks. Based on this index, a hierarchical adaptive coordinated control architecture is developed, utilizing sliding-mode control for active front-wheel steering to follow trajectories and a fuzzy-logic yaw moment controller to maintain stability. To prevent over-control in safe driving regions, an adaptive weighting mechanism seamlessly adjusts the stability interventions according to the proposed index. Hardware-in-the-loop (HIL) experiments demonstrate that the proposed method lowers sideslip risks on low-adhesion tracks. During a variable-curvature slalom, it reduces the lateral RMSE by 15.08% and decreases the maximum additional yaw moment from 118 N·m to 32 N·m, thereby mitigating excessive control effort, minimizing steering conflicts, and structurally improving the actuation efficiency of the 4WID system. Full article

Accurate online tracking of state of charge (SOC) inconsistency in lithium-ion battery packs is essential for safety. It is equally critical for effective battery management in real-world operation. To achieve robust performance in dynamic battery environments characterized by temperature fluctuations and cell aging, a method combining enhanced Rime optimized-unscented particle filter (ERIME-UPF) with cubature smooth variable structure filter-varying boundary layer (CSVSF-VBL) is proposed. The cell mean-difference model is used to simulate the behavior characteristics of the battery module, including the hysteresis effect dynamic migration model, and the Rint model. First, module SOC is estimated using an ERIME-UPF, which adaptively adjusts the noise covariances of UPF via the enhanced RIME optimizer. Simultaneously, CSVSF-VBL employs the Rint model to estimate cell SOC inconsistencies, incorporating capacity and internal resistance coefficients into the second-order performance chattering to better capture cell inconsistency. Experiments focus on LiFePO₄ batteries under various inconsistencies, temperature, and aging states. The results show that ERIME-UPF achieves an average mean absolute error (MAE) of 0.33% for module SOC estimation, while CSVSF-VBL achieves a peak MAE of 3.28% for cell SOC estimation. Demonstrating superior accuracy and robustness in tracking SOC inconsistency under dynamic and degraded operating conditions. Full article

Mild hybrid electric vehicles (MHEVs) are a cost-effective solution for reducing fuel consumption and emissions in the automotive sector, offering a low-level electrification alternative to battery electric and plug-in hybrid vehicles. This study uses the Equivalent Consumption Minimisation Strategy (ECMS) to investigate the optimal sizing of P2 MHEV powertrain components and the individual contributions of hybridisation features such as regenerative braking, idling fuel cut-off, load shifting and electric torque assist. Parametric simulations were performed by varying the power of the electric motor and the capacity of the battery for standard driving cycles. The results show that total fuel consumption for the NEDC driving cycle can be reduced by up to 29%, with regenerative braking providing the largest contribution. The optimal electric motor power for mild hybrid applications was found to be in the 20–30 kW range, depending on the driving cycle. Full article

During the process of vehicle–grid interaction, the charging load of electric vehicles shows significant uncertainty, which is driven by multiple user behavior variables: including the differentiated characteristics of users’ daily travel needs, as well as personalized charging habits, random charging periods, and dynamic changes in charging power demands. To address the scheduling challenges arising from the uncertainty of electric vehicle loads in the interaction between electric vehicles and the power grid, this paper proposes a multi-objective optimization scheduling method for the interaction between electric vehicles and the power grid, which takes into account the uncertainty of power sources and loads. This method can enhance the economic operation level of the power grid, increase the acceptance capacity of renewable energy, and improve the stability of the system. Firstly, this paper proposes an improved K-means clustering algorithm, which combines Monte Carlo sampling to achieve the generation and reduction of scenarios for electric vehicle load and photovoltaic output. Secondly, a scheduling framework based on the vehicle–grid collaborative interaction mode is constructed, and a random optimization scheduling model for photovoltaic storage electric vehicles is established. Finally, an example of a photovoltaic storage charging station in an industrial park is used for verification. The simulation results demonstrate the economic feasibility and effectiveness of this scheduling strategy. Full article

This work presents the results of an on-road test campaign on an aged mid-size battery electric vehicle. After a full charge, the vehicle was completely discharged by driving on the road, with different routes (combining speeds and road slopes) and payloads. The resulting driving range and discharged battery energy were measured. The results are compared with those obtained from previous laboratory test campaigns on a chassis dynamometer driving at constant speed or with the standardised testing protocols according to the WLTP. Considerations of the influence of environmental and route conditions on the usable battery energy during the on-road test are made. The new concept of virtual distance related to V2X applications is presented based on the UN GTR No. 22 dealing with in-vehicle battery durability. This is a new concept introduced to account for the additional ageing caused by battery cycling due to applications other than driving or charging. Full article

Inspired by the practical need for reliable drone-assisted logistics under varying weather conditions, this study investigates the vehicle–drone collaborative routing problem with weather constraints and time windows. The objective is to minimize the total delivery cost, including vehicle fixed costs, vehicle travel costs, drone flight costs, and time window penalty costs. To capture the impact of weather conditions on drone operations, a wind-speed-dependent dynamic flight speed function is introduced. A mathematical model is formulated, and an adaptive large neighborhood search algorithm integrated with genetic operations is proposed to enhance both local search efficiency and global exploration capability. Computational experiments on benchmark instances demonstrate that the proposed algorithm obtains high-quality solutions across different problem scales. Compared with the adaptive large neighborhood search algorithm and the improved genetic algorithm, the proposed approach reduces the optimal total delivery cost by an average of 4% and 2%, respectively. Sensitivity analysis further shows that increasing wind speed levels and the proportion of no-fly periods reduces the number of drone service tasks and increases total system cost, highlighting the significant impact of weather conditions on vehicle–drone collaborative delivery systems. Full article

The proliferation of vehicle-to-grid (V2G) charging stations in distribution networks introduces both voltage regulation challenges and untapped reactive power resources. This paper proposes a reactive–active power coordination control strategy for grid-forming (GFM) V2G charging stations to achieve voltage regulation in radial distribution networks. First, a voltage–reactive power sensitivity matrix is analytically derived from the linearized DistFlow equations, quantifying the voltage influence of each V2G station. The sensitivity matrix is computed from the network topology and line parameters, and its accuracy under varying operating conditions is validated against nonlinear power flow solutions. Second, a dynamic residual reactive capacity model exploits the inverter apparent power margin without curtailing active power, and a sensitivity-weighted proportional allocation distributes the reactive power references among stations. Third, a two-timescale hierarchical control architecture is designed: the upper layer solves a quadratic programming problem every 60 s to determine optimal set-points, while the lower layer employs GFM droop control with a 1 ms response to track references and provide inertia support. Simulation results on a modified IEEE 33-bus system demonstrate that the proposed method reduces the maximum voltage deviation by 62% compared with active-power-only control, while maintaining a frequency nadir of 49.73 Hz, confirming negligible frequency performance degradation. Extended simulations covering a 24 h period with stochastic EV arrival and departure patterns as well as varying load conditions further confirm the robustness of the proposed strategy. Full article

To address the complexities inherent in evaluating electric motorcycle ride comfort across diverse road profiles and operating speeds, this study establishes a systematic evaluation framework utilizing a multi-level fuzzy comprehensive assessment approach. Empirical investigations were conducted on asphalt, Belgian block, and speed-bump terrains at varying velocities. Triaxial acceleration data were acquired from the seat, footrest, and handlebar interfaces to compute weighted Root Mean Square (RMS) acceleration, Vibration Dose Value (VDV), and Power Spectral Density (PSD). By synthesizing subjective ratings, a correlation between tactile perception and objective metrics was derived to calibrate the two-level fuzzy model. Analysis reveals that vibration energy is predominantly concentrated in the vertical low-frequency domain (0–20 Hz) independent of test conditions. Notably, a 50% increase in velocity precipitated a 22.4% decrement in the comprehensive ride comfort index, degrading the classification from “Moderate” to “Fair.” The proposed framework provides a rigorous quantitative paradigm for vibration mitigation strategies and informed speed management in electric vehicle engineering. Full article

Electric vehicle (EV) adoption in South Africa remains constrained by high upfront purchase costs, limited charging infrastructure, and policy uncertainty, creating a need for lower-cost and locally relevant pathways to transport decarbonisation. This study evaluates the feasibility of converting a legacy light commercial pickup platform from internal combustion engine (ICE) propulsion to battery-electric propulsion through integrated component sizing, longitudinal vehicle simulation, and techno-economic assessment. A retrofit architecture comprising a traction battery, inverter-controller, electric motor, and DC-DC converter was developed using first-principles vehicle dynamics and energy-demand analysis. The resulting configuration employed a 40 kW AC induction motor, an approximately 28 kWh battery pack, a 40–60 kW inverter with 60 kW peak capability, and a 0.75–1.2 kW auxiliary DC-DC converter. Simulation over a representative 1000 s drive cycle showed stable speed tracking, sustained vehicle motion over approximately 10 km, and peak battery currents exceeding 300 A during acceleration, while regenerative braking reduced net cumulative energy consumption relative to gross demand. The economic analysis indicated that the retrofit pathway yielded the lowest cumulative total cost of ownership over most of a 10-year horizon, with breakeven relative to the used ICE baseline occurring at approximately 3.4 years. Lifecycle analysis further showed that the retrofit configuration achieved the lowest combined production and operational carbon burden among the compared vehicle pathways. These findings indicate that ICE-to-EV retrofitting of legacy light commercial vehicles can provide a technically feasible, economically competitive, and environmentally advantageous electrification strategy for South Africa and comparable emerging markets. Full article

CO₂ emissions are rapidly rising with new records, and the transport sector is considerably contributing to GHG emissions. The critical transition towards electrification and sustainable development demands a radical change in the transport industry. One of many solutions is to analyze the environmental benefits of optimized vehicle production and recycling of the vehicle components after their usable life to reduce dependency on limited raw materials. The electric motor is one of the most crucial powertrain components, yet studies on the overall ecological profile of production and the end of its usable life are limited. This study examines the life cycle assessment (LCA) impacts of electric motors used in passenger cars and the potential recycling of their materials. The analysis covers the production and recycling of components, crucial elements, and permanent magnets. The results show that housing and rotor production have the highest impacts, mainly due to the presence of steel, aluminum and permanent magnets. The findings discuss e-motor recycling innovations, state-of-the-art methods and emission-reduction potentials of recycling. This paper also covers the understanding that a significant transformation to optimize the resource consumption in the manufacturing of crucial vehicle powertrain components and reduce waste after end-of-life could bring combined ecological advantages. Full article

The performance attributes of a heterogeneous multi-battery pack system significantly impact the electric vehicle's performance. This study aims to investigate the power reduction and energy utilization phenomena in heterogeneous battery pack configurations that arise due to an uneven current split, focusing on defining the power ability curves and usable energy for the mixed system. A Multiphysics-based system model has been developed to investigate the factors contributing to power loss and usable energy when the aged packs are mixed with fresh packs. Different methods, viz., scaled, aged, and interpolation, are proposed to estimate the power retention curves for one and two fresh packs mixing into the homogeneous system. Also, energy evaluation helps in identifying the impact on vehicle range, which is an important attribute of vehicle performance. Altogether, having power ability curves and usable battery energy (UBE) for a heterogeneous multi-pack system helps in defining the decision-making strategies for the refurbishment of ESS during replacement and maintenance activities in EVs. Some strategies are introduced at the end using aged and scaled methods to conduct the most conservative power estimations while pack mixing. Energy evaluation is performed at the ESS level, highlighting the impact of fresh pack on the aged system usable energy. Full article

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