World Electr. Veh. J., Volume 17, Issue 3 (March 2026) – 54 articles

Energy Management Strategies (EMSs) are crucial for enhancing fuel economy and reducing emissions in light commercial vehicles (LCVs). This paper presented three EMS approaches for LCVs with hybrid powertrains: Rule-Based Control (RBC) and two optimization-based strategies, the Equivalent Consumption Minimization Strategy (ECMS) and Model Predictive Control (MPC). To enhance robustness under varying operating conditions, optimization algorithms were designed and tuned using the WLTC City driving cycle, and adaptive components were included. For a fair assessment of overall efficiency, all strategies were compared under identical constraints on hydrogen and electrical energy consumption. The results showed that, under these constraints, MPC achieved the longest driving distance, highlighting its superior energy utilization capability. In a broader comparative analysis, both the ECMS and MPC outperformed the benchmark RBC, with MPC demonstrating the most consistent performance, enhanced stability, and strong adaptability in dynamic scenarios. The findings indicate that MPC offers notable advantages for LCV energy management, combining efficiency, robustness, and interpretability, positioning it as a promising candidate for practical implementation in future hybrid powertrain systems. Full article

This study maps competency requirements for two representative frontline marketing roles in China’s new energy vehicle (NEV) sector, NEV sales consultants and livestreaming hosts, and examines their alignment with current vocational education standards. Using a market-oriented, data-driven design, recruitment texts were collected from Zhaopin across more than 20 major Chinese cities. Latent Dirichlet Allocation (LDA) identified competency themes, which were then organized into work-process task domains and visualized as position–task–competency mappings. Mapping these demand-side requirements to national teaching standards reveals relatively strong alignment for sales in market insight and sales strategy, but also gaps in omni-channel lead operations, customer experience management, and operational coordination; livestreaming roles show systematic gaps across the entire work process, particularly in on-air control, customer conversion process design, and data-driven optimization. Building on the identified gaps, the study proposes a position–task–competency-to-curriculum translation pathway to support modular updates in NEV marketing talent development within vocational education and training. Full article

This paper presents an exploratory cross-cultural analysis of autonomous vehicle expectations through a 57-question survey distributed in the United States (n = 50), Germany (n = 66), and Panama (n = 41). Five scales are presented and validated: Driving Behavior Aggressiveness (DBA), Self-Driving Car Aggressiveness (SDCA), Artificial Intelligence (AI) Trust (AIT), AI Driving Mechanics Trust (AIDMT), and Driver Safety Score (DSS). Each scale is validated via confirmatory factor analysis and multi-group measurement invariance testing. Results show that drivers prefer a self-driving car driving style more conservative than their own; however, participants who are more trustful of AI show DBA–SDCA equivalence, consistent with acceptance of a driving style comparable to their own. Significant cross-cultural differences emerge, with Panama diverging from the United States and Germany on DBA, SDCA, AIDMT, and DSS; these country effects largely persist after controlling for demographics. These findings suggest that self-driving car behaviors should be tailored to regional expectations and passenger trust profiles to improve adoption. Full article

The implementation of the new national electric bike (e-bike) standard has imposed stringent compliance requirements on equipment and e-bikes in the instant delivery sector, which directly affects the delivery efficiency and the work adaptability of food delivery riders. This study aims to investigate food delivery riders’ continued usage intention of e-bikes under China’s new e-bike regulation. Based on valid data collected from food delivery riders in Nanjing, this study employs ordered logit regression to examine the primary factors influencing their continued usage intention of e-bikes. The findings reveal that: (1) Male riders’ willingness to continue using e-bikes is comparatively lower, whereas older riders show a stronger intention. (2) Food delivery riders with higher incomes and those who need to replace their e-bikes show a stronger inclination to continue using them. (3) Limited e-bike options have a significant negative effect on riders’ continued usage intention, while speed limits exert no significant influence. Based on these empirical findings, corresponding policy recommendations are proposed to promote riders’ continued use of e-bikes, such as developing age-friendly delivery models, establishing an income guarantee mechanism for riders, and optimizing platform delivery time allocation. The findings could provide a theoretical basis and practical insights for policymakers and food delivery platforms to improve e-bike management policies. Full article

To further explore the energy-saving potential of power-split hybrid electric vehicles, this paper addresses issues in traditional Radial Basis Function (RBF) neural network-based vehicle speed prediction methods, which rely solely on time-varying information from historical speed sequences of the host vehicle, leading to problems such as idle overestimation, large local prediction errors, and low prediction accuracy across different time horizons. An improved RBF neural network-based vehicle speed prediction method that integrates multi-sensor information is proposed. This method identifies the driver’s driving intention through a fuzzy inference system, extracts historical speed sequences within a fixed time window in a rolling manner, and integrates inter-vehicle motion characteristic parameters obtained through fusion of millimeter-wave radar and camera data. These multi-dimensional influencing factors are used as inputs to the RBF neural network for vehicle speed prediction. Based on this, an energy management optimization model for the vehicle is established, with the goal of optimizing fuel economy. The model predictive control (MPC) strategy is employed, and the Dynamic Programming (DP) algorithm is used to solve for the real-time optimal torque distribution among various power sources within a limited time horizon. Finally, simulation validation is conducted on the MATLAB/Simulink platform under the CHTC-B driving cycle, CCBC driving cycle, and actual road driving cycle. The results show that, compared with the traditional method adopting Radial Basis Function (RBF) neural network-based vehicle speed prediction and rule-based energy management, the proposed method improves the vehicle’s fuel economy by 4.11%. Full article

This paper proposes a multi-objective optimization method based on response surface methodology and genetic algorithm to address the electromagnetic noise issue in external rotor permanent magnet synchronous motors. Theoretical analysis and 2D finite element simulation of electromagnetic force were conducted to identify the main orders of electromagnetic force; subsequently, through motor load and no-load tests, it was determined that the 6th-order radial electromagnetic force is the primary source of electromagnetic noise. Taking the 6th-order radial electromagnetic force, average torque, and torque ripple as optimization objectives, three key structural parameters were selected from eight optimization variables to construct a response surface model. The structural parameter optimization scheme for the motor was then obtained using a genetic algorithm. Finally, the optimization scheme obtained by the response surface method was validated under motor load conditions using two-dimensional finite element simulation; simulation results indicate that, compared to the original design, the optimized motor, exhibits a reduction in torque ripple by 65%, with the harmonic content of the radial air-gap flux density at the 1st, 3rd, 5th, and 7th orders decreasing by 8.7%, 6.4%, 12.5%, and 10.7%, respectively, and the 6th-order radial electromagnetic force reduced by 16.4%. Based on experimental identification of the dominant noise source, this reduction is expected to effectively suppress electromagnetic noise, which will be validated on a prototype in future work. Full article

Reducing the life-cycle CO₂ emissions of electric vehicle (EV) traction motors requires a comprehensive evaluation of material selection, magnet configuration, and structural design. In this study, six motors—including a benchmark NdFeB-based PMSM—are designed under unified constraints of identical outer diameter, ampere-turns, and target torque (163 Nm), enabling a fair comparison of environmental performance. Electromagnetic field simulations are conducted to optimize each design, and life-cycle CO₂ emissions are quantified using emission factors from IEEJ-IAS and standard material databases. The results show that manufacturing-stage emissions vary significantly depending on magnet and winding materials: the benchmark PMSM exhibits the highest manufacturing CO₂ (42.1 kg-CO₂), while the rare-earth-free PMaSyn.RM achieves the lowest value (28.4 kg-CO₂). In contrast, use-stage emissions over 150,000 km are dominated by motor efficiency, ranging from 1820 kg-CO₂ (PMSM-Cu) to 2030 kg-CO₂ (Al-wound PMSM). Consequently, the total life-cycle CO₂ spans from 1848 kg-CO₂ (PMaSyn.RM) to 2072 kg-CO₂ (Al-wound PMSM), indicating that rare-earth-free motors minimize manufacturing impact, whereas high-efficiency PMSMs reduce use-stage emissions. Furthermore, the study evaluates the practical feasibility of aluminum windings and rare-earth-free designs, identifying structural requirements such as dual-rotor configurations for aluminum conductors and flux-barrier optimization for ferrite-based motors. These findings provide quantitative insights into the trade-offs between material sustainability and operational efficiency, offering guidance for future EV motor development toward carbon neutrality. Full article

Battery-powered electric unmanned surface vehicles (e-USVs) and electrified surface-cleaning platforms require reliable onboard vision under strict compute and power constraints. In reflective water environments, tiny floating debris is often obscured by specular highlights, reflection bands, ripples, motion blur, and camera jitter, while label noise further degrades training stability. To improve robustness without increasing onboard inference burden, this paper proposes YOLOv11-IMP, a lightweight detector for reflective water-surface scenes and embedded edge inference. The method integrates a transformer-enhanced backbone stage, a Global Channel–Spatial Attention module in the neck, and a median-enhanced channel–spatial module in the neck to improve global-context modeling, cross-scale interaction, and weak-boundary representation. WIoU-v3 is adopted to improve localization, and a train-time-only noise-aware screening strategy based on the small-loss principle is introduced to suppress unreliable labels without extra inference cost. Experiments on the CAS dataset and a self-built debris dataset show gains of 3.3% in AP@0.75 and 6.5% in AP for small objects over YOLOv11, while maintaining 7.3 GFLOPs and real-time inference on Jetson Nano, demonstrating practical potential for energy-constrained onboard missions. Full article

Reliable steering regulation under varying road curvature and actuator constraints remains a central challenge in autonomous electric vehicles (AEVs). Many exiting approaches rely on reactive error correction or treat preview information solely as a reference adjustment, limiting anticipation and physical consistency. This study proposes a prediction–preview steering control (PSC) framework in which future curvature information within state propagation and constraint handling enables forward-looking steering decisions while respecting dynamic and actuator limits. The method is evaluated using a lateral-heading vehicle model with real-road geometric variation. Experimental results indicate significant improvement in tracking performance, reducing lateral RMSE from 0.1747 m to 0.0074 m with a maximum deviation of 0.0889 m and limiting heading RMSE to 0.0867° (maximum 1.2046°). Steering angle commands remain bounded within ±8.7°, while steering angle rate is maintained within 40–60°/s, ensuring smooth and dynamically admissible operation. The proposed strategy offers a computationally efficient solution for embedded AEV steering systems and demonstrates improved robustness under practical curvature transitions. Full article

This study presents a novel cooperative control strategy designed to mitigate torque ripple and enhance the disturbance rejection capability of switched reluctance motors (SRMs). The proposed approach integrates model predictive control (MPC) with direct instantaneous torque control (DITC), leveraging the torque sharing function (TSF) to generate phase-specific reference torque profiles. MPC employs rolling optimization to compute the optimal duty cycle in real time, achieving low torque ripple and consistent switching frequency during steady-state operation. To overcome the inherent delay in MPC’s dynamic response, DITC is incorporated as a fast-acting compensation loop that activates immediately upon detecting abrupt variations in speed or load, thereby delivering rapid torque adjustment and reinforcing system resilience. For validation, an 8/6-pole SRM control model was developed using Ansys/Maxwell and MATLAB/Simulink, and subjected to multi-scenario simulations. The results reveal that, compared to conventional MPC, the proposed method reduces steady-state torque ripple by 19.4% and shortens dynamic recovery time by 40%, demonstrating superior torque smoothness and improved robustness against external disturbances. Full article

This paper proposes a hierarchical control framework designed to enhance the path tracking accuracy of intelligent electric vehicles under diverse operating conditions. For lateral control, an improved model predictive control strategy is developed, utilizing a fuzzy inference system for parameter initialization and a Deep Deterministic Policy Gradient algorithm for online adaptive tuning. For longitudinal control, a proportional–integral–derivative controller is optimized via a hybrid genetic algorithm–particle swarm optimization method. Co-simulations conducted in CarSim/Simulink under straight-line, double-lane-change, and double-sine-wave maneuvers demonstrate that the proposed framework significantly reduces lateral deviation and heading error while ensuring smoother actuator response. Compared to conventional MPC and PID controllers, the proposed method reduces maximum lateral error by over 50% and settling time by 60%, confirming its effectiveness and robustness in complex tracking scenarios. Full article

Electric vehicle (EV) chargers play an important role in the popularity of electric vehicles. In order to improve the efficiency of EV chargers, this paper replaces the discrete inductors in an interleaved Boost PFC topology with a coupled inductor. Theoretical analysis of the Boost PFC topology was presented, and the coupled inductor was designed, with simulation verification. Experimental testing of the designed coupled inductor was done on a 7 kW EV charger platform. The experimental results show that the designed coupled inductor can improve the efficiency of an EV charger. Full article

Accurate and reliable localization is crucial for advanced autonomous driving systems. Traditional high-precision localization approaches rely on meticulously annotated high-definition (HD) maps and employ visual-geometric methods to derive accurate pose information. However, the construction, maintenance, and updating of HD maps are costly and time-consuming. In contrast, localization using publicly available navigation maps provides a low-cost and scalable alternative. Existing methods typically align BEV (Bird’s-Eye-View) features extracted from surround-view images with navigation maps to obtain localization results. Although such approaches can achieve high accuracy, they often neglect the inherent modality gap between BEV features and navigation maps, leading to localization errors. To address this issue, we propose NMLoNet: An End-to-End Intelligent Vehicle Localization Network Using Navigation Maps. The proposed method exploits road semantic elements to effectively bridge the modality gap between BEV representations and navigation maps. Specifically, a Deformable Attention Module is introduced after BEV feature extraction to capture long-range dependencies among BEV features. Furthermore, we innovatively incorporate vector map constraints to minimize the discrepancy between BEV and navigation map features. In addition, a multi-level cross-modal feature registration mechanism is designed to achieve more precise alignment between BEV and map representations. Extensive experiments on the nuScenes and Argoverse datasets demonstrate that NMLoNet achieves state-of-the-art performance, improving localization accuracy by approximately 11% under monocular settings and 24% under surround-view configurations. Moreover, the proposed network maintains robust localization performance in complex and highly dynamic driving environments. Full article

Battery state-of-health (SoH) estimation is central to transportation electrification because it conditions safety limits, warranty accounting, power capability management, and long-horizon fleet optimization. Although deep temporal architectures can achieve high laboratory accuracy, field deployment is frequently limited by laboratory (Lab)-to-field (L2F) domain shift that alters input statistics, feature definitions, and noise regimes. Under such a shift, predictors may remain strongly monotonic, preserving degradation ordering and become operationally unreliable due to systematic output distortion (e.g., compression/warping of the SoH scale). A deployment-complete L2F transfer learning pipeline is presented, built around a gated Temporal Convolutional Network (TCN)–Transformer fusion backbone, domain-specific adapters and heads, alignment-regularized fine-tuning, and row-level inference via sliding-window overlap averaging. To address the dominant deployment failure mode, a Safe Calibration stage robustly filters calibration pairs and selects among candidate calibrators under a strict do-no-harm criterion. On an unseen deployment stream (2154 labeled rows), overlap-averaged raw inference achieves MAE = 0.0439, RMSE = 0.0501, and R² = 0.7451, consistent with mid-to-high SoH range compression, while Safe Calibration (Isotonic-Balanced selected) corrects nonlinear scaling without violating monotonic structure, improving to MAE = 0.0188, RMSE = 0.0252, and R² = 0.9357 to obtain a complete understanding of the challenges due to domain shifts, evaluation is extended to include other architecture baselines such as TCN-only, Transformer-only, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and a Ridge regression baseline. Also added is explicit alignment and calibration ablations that include CORAL off/on, that is, none vs. Safe-Global vs. Context-Aware under identical leakage-safe splits and the same overlap-averaged deployment inference operator. This work goes beyond peak-score reporting and looks at the robustness of a pipeline under domain shift, which is quantified across four random seeds and multiple deployment streams, with uncertainty summarized via mean ± std and bootstrap confidence intervals for Mean of Absolute value of Errors (MAE)/Root of the Mean of the Square of Errors (RMSE) computed from per-example absolute errors. Full article

Electric vehicle charging stations (EVCSs) are a critical infrastructure in urban areas. However, because they depend on power grids and digital networks, they are prone to disruptions from grid failures, extreme weather, and cyber threats. Ensuring resilience is therefore essential to minimise service disruptions and ensure reliable transportation in urban areas. While interest in EVCS resilience is growing, current studies are dispersed across technical, environmental, and spatial domains, limiting a consolidated understanding of how resilience is conceptualised and assessed in urban areas. Despite this growing body of research, no prior systematic review has comprehensively synthesised resilience-specific evidence for EVCSs in urban areas. Thus, the objective of the study was to systematically synthesise empirical research on resilient EVCSs in urban areas to identify key factors influencing resilience and how resilience is assessed. A systematic literature review was conducted on 52 empirical articles from Web of Science and Scopus published between 2015 and 2025, following the PRISMA protocol. The review revealed an increasing trend in publications over time, with research geographically concentrated in Asia, the United States of America, and Europe. Results also showed that the resilience of EVCSs in urban areas is influenced by context-related factors (such as location, environment, and governance) and system-related factors (such as operational, technical, and financial), with location and technical issues being the most studied. The resilience of EVCSs is mainly assessed through accessibility, capacity, availability, and vulnerability, using tools such as indices, curves, scenarios, and optimisation models. However, gaps remain in governance, environment, modular design, predictive maintenance, social aspects, and developing economies. Future research should focus on integrating governance and equity into EVCS planning and developing modular, renewable-powered charging systems supported by smart technologies to enhance resilience in urban areas, particularly in developing economies. This review proposes a Factors-Dimensions Implementation framework that operationalises established resilience concepts by linking context- and system-related factors to measurable resilience dimensions of EVCSs in urban areas. Full article

With the rapid development of all-electric ships (AESs) and the growing emphasis on sustainable shipping, there is an increasing need for effective scheduling solutions that address the unique challenges associated with AESs, such as battery limitations and charging infrastructure constraints. However, existing studies primarily focus on simplified scenarios, overlooking the complexities inherent in multi-port and multi-vessel shipping networks. To bridge this gap, this paper develops a Mixed-Integer Linear Programming (MILP) model aimed at minimizing total operational costs, specifically targeting the scheduling optimization problem in heterogeneous fleet feeder shipping networks, while explicitly considering charging requirements and time window constraints. To tackle the computational challenges posed by large-scale and strongly constrained scenarios, this study designs an optimization algorithm based on Adaptive Large Neighborhood Search (ALNS), incorporating a two-stage strategy and a destroy–repair mechanism to progressively refine solutions. Based on data from the Yangtze River feeder network, numerical experiments demonstrate the feasibility and effectiveness of the proposed model and algorithm. Additionally, a sensitivity analysis on battery capacity explores the effects of variations in key technical parameters on all-electric ship utilization and overall operational costs. Full article

Electric vehicle headlamp illuminance directly affects the driver’s visibility. Accurately predicting electric vehicle headlamp illuminance is crucial to enhancing driving safety. Existing deep learning models are trained using data collected from real-world road testing, yet external factors may compromise its reliability. Electric vehicle headlamp illuminance prediction primarily relies on data fitting, and such models are prone to overfitting when input data are affected by external disturbances. To solve the problem, we propose a luminancxel properties physical information neural network (LumiPINN) prediction model. Test conditions are designed in accordance with standard. The data was collected in an indoor laboratory to eliminate the influence of external factors, then underwent cleaning and pre-processing to ensure data quality. During the modelling process, the physical model is treated as a constraint, with the loss function to jointly optimise the prediction model. Compared with Deep Neural Network and Artificial Neural Network prediction models, the Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Relative Error were reduced by 60.2%, 83.6%, 59.6%, 61.3%, and 71.7%, 90.7%, 69.5%, 71.4%. The Coefficient of Determination improved by 0.0015 and 0.0029. The results show that the LumiPINN prediction model demonstrates higher accuracy in prediction outcomes. Full article

Model predictive current control (MPCC), owing to its straightforward design and convenient multi-objective optimization, has been widely adopted in applications demanding high dynamic performance. However, the conventional MPCC suffers from poor current steady-state performance and severe parameter dependence. To address these issues, this paper proposes a virtual-vector based model-free predictive current control (MFPCC) scheme for permanent magnet synchronous machine (PMSM) drives with adaptive control-gain. The proposed approach is developed based on the ultra-local model (ULM) concept to simplify the control structure and enhance robustness. The disturbance is observed by a linear extended state observer (LESO) and the effect of control-gain deviation on disturbance observation is analyzed. In addition, a control gain adaptive method is introduced to weaken the high-frequency components of the integrated disturbance, which can further improve the performance of observer. Furthermore, the virtual-vector control set is built where symmetrical vector sequences are included to reduce torque ripple. An improved optimization strategy is also developed that reduces computation and improves steady-state performance. Comprehensive experimental results confirm the effectiveness and superiority of the proposed method in terms of steady-state performance, robustness, and computational burden. Full article

Traffic sign recognition is critical for intelligent transportation systems and autonomous driving. Conventional convolutional neural networks (CNNs) typically utilize the ReLU activation function for its computational efficiency; however, alternative activation functions can improve computing effectiveness capacity in recognition tasks. In this study, we propose a CNNs model enhanced with the Gaussian Error Linear Unit (GELU) activation function. We evaluate its performance on benchmark datasets and compare it against both ReLU and Leaky ReLU baseline. Experimental results show that the proposed GELU-activated CNNs achieves a recognition accuracy of 99.75% and provides small but consistent improvements over ReLU and Leaky ReLU models, particularly under challenging conditions such as occlusion and low lighting. These findings highlight GELU’s potential to enhance the robustness and reliability of traffic sign recognition in Electric Vehicles for autonomous driving applications. Full article

In Germany, a shortage of public charging stations for the significantly increasing number of electric cars exists. This shortage, along with the associated range anxiety, poses a hurdle for the market entry of electric cars. In addition, the construction of public charging stations for electric vehicles is outside the control of vehicle owners. Accelerating this process is challenging due to regulatory and other considerations. This paper presents a novel scientific and technical approach to complement existing public charging infrastructure by integrating privately owned charging stations into a publicly accessible ecosystem. The core of our work explores the integration of private charging stations into the application ecosystem, clarifying the reservation process and addressing potential challenges. Furthermore, we provide a comprehensive overview of the features related to routing and locating near charging stations. We examine the potential challenges that may arise during the practical implementation of the proposed system. The technical feasibility of the approach is validated through implementation and simulation, demonstrating the practical applicability of the proposed system and its ability to support real-world usage scenarios. The results indicate that such a system has significant potential to enhance the accessibility and usability of charging infrastructure, thereby promoting sustainable mobility and lowering the entry barriers for electric vehicles. By providing integrated charging information, reservation functionality, and route planning, the proposed solution contributes to increasing user acceptance and supporting the transition toward more environmentally friendly transportation. Full article

As the number of electric vehicles (EV) increases, the demand for recommending the best charging location when using a large-scale charge network to charge is also increasing. A successful recommendation will utilize the user’s preference and the operational constraints of the charging network to make sure that it also takes into account the real-time operational requirements of the network. Most current papers focus on optimizing individual algorithmic components in isolation; consequently, many of these papers neglect to provide a holistic view of an integrated system. In addition, there are many operational requirements that current research does not consider, such as cold-start personalization for new users and enforcing real-time operational constraints like station availability, power capacity, maintenance windows, etc. This paper describes a deployable multi-stage recommendation system that creates a candidate list based on location and ranks preferences based on user, station and context features. The recommendation system also adds a configurable rule-based re-ranking layer to ensure that both hard constraints (i.e., charger availability and power-cap limits) and soft objectives (i.e., load balancing and operator priority) are enforced. A method for enabling mixed use between stable Bayesian and adaptive Bayesian methods was developed to provide users starting with cold-start performance that do not have adequate histories. Evaluation of this method using 100k+ real charging sessions showed that the fraction of sessions where the ground-truth station appears in the top-two recommendations (Hit@2) for the recommendation system was 0.82, representing a 37% increase in performance compared to proximity-based recommendation methods. The online deployed recommendation system has a 99th-percentile serving latency (P99) of less than 200 ms. The findings of this paper provide a framework for the implementation of operationally-relevant user-centric recommendation systems for EV services at scale. Full article

Under the dual impact of environmental issues and the energy crisis, new energy vehicles (NEVs) have gradually become a phenomenal emerging industry in China, also essentially becoming a new engine to support the growth of China’s economy. While topics related to the NEV industry have gained widespread attention, there is a lack of studies specifically focusing on the characteristics of its industrial spatial distribution pattern. Based on the data related to NEV-listed companies located in the Yangtze River Delta (YRD) region in 2022, this study constructs the corresponding city network using the method of social network analysis (SNA) and interprets the structural features of this network. The results reveal the following: (1) The network exhibits three fundamental characteristics: low density, short path length, and multiple centers. (2) The NEV industry in the YRD has formed the agglomeration pattern of three major “clubs”, projected on the map in the shape of a “golden bow”. (3) Cities in the YRD show a “pyramid-type” collaboration in the NEV industry. (4) Collaboration between cities in the NEV industry can cross the limits of geographic proximity and even administrative boundaries. Full article

Compared with rule-based and optimization energy management strategies, online optimal energy management control strategies for a dual-mode power-split hybrid electric vehicles (PSHEVs) are able to achieve better fuel economy and real-time performance. Global online optimization of a finite time domain energy management strategy based on the hybrid model predictive control (HMPC) algorithm is proposed in this study. To reduce the computing time, a linearized predictive model is built; because dual-mode PSHEVs can be considered hybrid systems that include continuous and discrete states, the hybrid states can be expressed uniformly. Therefore, a mixed logical dynamic (MLD) predictive model is built based on hybrid system theory, and an HMPC energy management strategy is proposed based on the MLD predictive model. To solve the optimal control problem online to obtain the optimal control sequence, the optimal control problem is converted into a mixed-integer linear programming (MILP) problem. The HMPC-based energy management strategy is compared with dynamic programming (DP)-based and rule-based energy management strategies over two different driving cycles. Simulation results indicate that the HMPC-based EMS achieves 80.60% and 83.79% of the fuel economy performance obtained by the DP-based EMS. In comparison, the rule-based EMS only achieves 66.46% and 70.51% of the DP-based control performance. Therefore, the HMPC-based energy management strategy is favorable for real-time control while effectively improving fuel economy. Full article

To improve the accuracy and real-time performance of trajectory tracking control for a four-wheel differential drive intelligent sweeping vehicle, a trajectory tracking control method based on an adaptive strong tracking extended Kalman filter (ASTEKF) state estimator and a Laguerre-based model predictive controller (LMPC) is proposed. Based on the kinematic model of the intelligent sweeping vehicle, an ASTEKF state estimator is designed for vehicle state estimation, and a Laguerre-function-based model predictive controller is developed for trajectory tracking control, thereby enhancing the control accuracy and stability of the vehicle. Simulation results demonstrate that compared with the conventional MPC algorithm, the proposed ASTEKF–LMPC algorithm reduces the maximum lateral error by 44.65% and the maximum heading angle error by 40.96% during sweeping operations, while under normal driving conditions, the maximum lateral error and maximum heading angle error are reduced by 36.27% and 40.03%, respectively. Furthermore, experimental tests conducted on an intelligent sweeping vehicle platform show that the proposed method reduces the maximum lateral error by 34.25% and the maximum heading angle error by 23.18%, thereby validating the effectiveness of the proposed algorithm in intelligent sweeping operations. Full article

The fast growth of electric vehicles (EVs) and renewable energy motivates reliable charging infrastructure with balanced energy management and good power quality. However, conventional converter controllers like proportional and integral (PI) and fuzzy logic controllers (FLCs) exhibit slow dynamic response, poor adaptability to varying solar conditions, unbalanced energy management, low power quality, and higher total harmonic distortion (THD). To overcome these limitations, this work proposes an adaptive neuro-fuzzy inference system (ANFIS) controller for balanced energy management and improved power quality in EV charging stations. The ANFIS controller is a combination of a fuzzy inference system (FIS) and a neural network (NN). The FIS provides the best maximum power point tracking and robust control during changing solar PV conditions. The NN optimally controls the flow of power between the solar PV system, energy storage battery (ESB), EV, and utility grid. The entire system is simulated in MATLAB/Simulink. It consists of a PV system with a capacity of 2 kW, an ESB with a capacity of 10 kWh and an EV battery with a capacity of 4 kWh, which are linked by bidirectional DC/DC converters. A 30 kVA bidirectional inverter, along with an LCL filter, is connected between the 500 V DC bus and 440 V utility grid, allowing for both directions. The results validate the effectiveness of the proposed ANFIS controller in terms of DC bus voltage stability, faster dynamic response, enhanced renewable energy utilization, improved efficiency to 98.86%, reduced voltage and current THD to 4.65% and 2.15% respectively, reduced utility grid stress, and enhanced energy management compared to conventional PI and FLCs. Full article

The year 2020 marked the eve of the explosive growth in China’s BEV market, which may lead to substantial carbon emission implications. This study quantifies the full life-cycle carbon emissions of battery electric vehicles (BEVs) across China’s 31 provinces using a multi-regional input-output-based life-cycle assessment (MRIO-based LCA) model, covering four phases: manufacturing, driving, battery replacement, and scrapping. Moreover, the coupling coordination degree (CCD) model was employed to evaluate the coordination degree between provincial BEV deployment and a green electric system. Results show that the total carbon emissions amount to 48.95 million tons, with manufacturing contributing 58.4% and driving for 33.4%. Hebei (5.72 million tons) and Shandong (4.16 million tons) account for the largest shares, driven by embodied emissions from heavy industry and coal-intensive power systems. Interprovincial embodied carbon flows reveal a dominant north-to-south transfer pattern. Furthermore, coupling coordination between BEV deployment and a green electric system is generally medium (0.5 < CCD ≤ 0.7), with Guangdong (CCD = 0.73) standing out as an exemplary case, demonstrating an effective equilibrium between BEV industry expansion and the integration of renewable energy. These findings highlight that in provinces with rapidly growing BEV industries, such as Guangdong, policies promoting low-carbon supply chains and accelerating green electricity infrastructure development are crucial to reducing emissions. Full article

Hybrid tractors operating in hilly terrain often suffer from reduced overall performance due to unregulated slip rate variations. To address this issue, this paper proposes a hierarchical cooperative control strategy that jointly optimizes traction efficiency and energy consumption. First, a traction force–slip rate coupling model is developed, and an adaptive slip rate control method is designed to determine the optimal traction force distribution range, thereby improving traction efficiency. Next, an equivalent consumption minimization strategy (ECMS) is formulated to minimize equivalent fuel consumption, using the torque coupler ratio and torque distribution ratio as optimization variables. These two methods are then integrated through slip rate and traction force as transfer variables, forming a hierarchical cooperative control framework that simultaneously considers both objectives. The proposed method is validated under plowing conditions through MATLAB simulations and Hardware-in-the-Loop (HIL) tests, using a fixed coordinated traction force allocation method as a benchmark. Results show that, compared to the benchmark, the proposed method reduces slip rate loss by 29.6%, increases traction efficiency by 8.7%, and decreases equivalent fuel consumption by 14.4%. This study provides new insights into improving the energy efficiency of hybrid tractors in complex terrains. Full article

This study analyzes 1441 industry–university–research (I-U-R) collaborative invention patents (2004–2023) in China’s new energy vehicle (NEV) industry using social network analysis. We propose the “Proximity–Industry Life Cycle” Fit Theory to systematically investigate the influence mechanisms of industrial proximity, geographical proximity, and technological proximity on the evolution of the industry–university–research collaborative innovation network of the new energy vehicle industry across three industry life cycle stages. Key findings include: (1) the network scale expanded significantly while density declined; (2) State Grid Corporation emerged as the core node after 2010; (3) all three proximity dimensions positively influence network evolution, with varying effects across stages—industrial proximity dominates in the emergent stage, while technological proximity becomes the primary driver in later stages. Policy implications: Governments should formulate stage-differentiated policies—encouraging industrial chain collaboration in early stages while promoting technology alliances in mature stages. Core enterprises should be supported to strengthen I-U-R collaboration, and cross-regional innovation platforms should be established to optimize proximity-driven knowledge transfer. Full article

This study provides a comprehensive assessment of the global implications arising from China’s dominant position in the electric vehicle (EV) transition. By 2030, under current policy trends, China is projected to account for approximately 57% of the global EV stock (238 million vehicles) and 53% of the worldwide EV-driven oil displacement (2.75 million barrels per day). Its demand for automotive batteries will reach 1516 GWh, representing 47% of the global total. Employing LMDI-I decomposition, we find that China’s outsized impact is driven not merely by the scale but by the higher vehicle utilization intensity (contributing 61% of its advantage) and policy support for efficient vehicle types like plug-in hybrids and two/three-wheelers (contributing 31%). The extreme geographic concentration creates a significant systemic risk; our Monte Carlo simulation indicates a 92% probability that a moderate supply shock in China would trigger a severe global battery shortage. Conversely, China stands to gain substantial economic benefits, estimated at USD 117 billion annually by 2030 (90% CI: 78–173 billion) from the avoided oil imports and potential carbon revenues. These findings highlight a central paradox of the energy transition: while China delivers immense climate and energy security benefits, its dominance introduces unprecedented supply chain vulnerabilities and a highly asymmetric distribution of economic gains, necessitating urgent policy responses for diversification and resilience. Full article