Search Results (13,228)

Connected and Automated Electric Vehicles (CAEVs) are rapidly evolving into complex Cyber-Physical-Social Systems (CPSS), generating structural tensions between technological innovation and public safety. Current research in public governance exhibits significant fragmentation. Scholars frequently isolate data privacy compliance from algorithmic safety auditing, treating them as distinct silos. To bridge this gap, this study applies the PRISMA framework to systematically synthesize 135 core peer-reviewed articles, exposing the endogenous limitations of unidimensional regulatory paradigms. Our analysis yields three central insights. First, traditional “notice-and-consent” models fail under the ubiquitous data collection demands of modern V2X environments. Macro-level policies must translate into foundational Privacy-Enhancing Technologies (PETs) through “Law-as-Code” mechanisms. Second, the opacity of end-to-end algorithmic decision-making deconstructs traditional tort liability systems. This necessitates ex-ante quantitative auditing mechanisms—such as Explainable Artificial Intelligence (XAI) and enhanced Threat Analysis and Risk Assessment (TARA 2.0)—to mitigate adversarial attacks and physical-level safety hazards. Third, overcoming cross-national regulatory fragmentation requires constructing a “dual-track synergistic” governance architecture. This framework institutionalizes the coupling of data lifecycle quality workflows with the algorithmic Safety of the Intended Functionality (SOTIF). Ultimately, this review advocates for adaptive regulatory sandboxes and advances the harmonization and mutual recognition of global standards (e.g., ISO/SAE 21434, UN R155/156). Addressing current methodological and empirical data constraints, future academic inquiry must pivot. Researchers should target the value alignment challenges of Large Language Models (LLMs) in autonomous driving and implement multi-stakeholder participatory policy pilots designed to reconcile diverse social values. Full article

The rapid expansion of electric vehicle (EV) adoption has increased the demand for efficient charging infrastructure and data-driven approaches for understanding charging behavior. Analyzing charging patterns and identifying abnormal charging sessions are essential for improving charging network reliability, infrastructure utilization, and operational efficiency. This study proposes a comprehensive machine learning framework for EV charging behavior analysis and anomaly detection using real-world charging session data collected from six charging bays. Four charging behavior indicators, namely energy consumption (Usage), charging duration (Duration), average charging output power (Average Output), and Energy Consumption Ratio (ECR), were extracted through a feature engineering process. K-Means clustering was employed to identify distinct user behavior groups, while Principal Component Analysis (PCA) was utilized to visualize cluster separability. Isolation Forest was subsequently applied to detect anomalous charging sessions and investigate abnormal charging behavior patterns. Statistical validation was conducted using Analysis of Variance (ANOVA), and Pearson correlation analysis was performed to examine relationships among charging features and anomaly occurrence. The results identified four distinct charging behavior clusters representing moderate users, regular users, inefficient users, and high-power users. Clustering validation achieved a silhouette score of 0.6086, while PCA retained 89.8% of the total variance using two principal components. An anomaly detection analysis revealed that inefficient charging behavior exhibited the highest anomaly occurrence, whereas regular users demonstrated highly consistent charging patterns. Analysis indicated that average charging output power and ECR were the most influential variables contributing to anomaly identification. ANOVA results confirmed statistically significant differences among all identified clusters (p < 0.001), while correlation analysis demonstrated a strong positive relationship between charging power and charging efficiency (r = 0.95). The anomaly detection framework achieved accuracy, precision, recall, and F1-score of 80.0%. The proposed framework provides a comprehensive approach for EV charging behavior characterization, anomaly detection, and charging infrastructure assessment. The findings can support charging network operators in improving charging efficiency, identifying abnormal charging activities, and enabling data-driven management of EV charging systems. Full article

Battery state estimation is the main safety constraint for electric rotary-wing unmanned aerial vehicles (UAVs): mission decisions depend on both the instantaneous State of Charge (SOC) and the Remaining Useful Life (RUL). The present study compares seven machine learning and deep learning models (LR, SVM, k-NN, GBT, EL, LSTM, and a simplified RWKV) on real flight data from a rotary-wing helicopter testbed with a Pixhawk autopilot and an NVIDIA Jetson Nano mission computer. The dataset has 1310 samples (∼262 s) of nine on-board sensor signals. Mission-based RUL is defined as the projected time until SOC reaches a $20\%$ safe-landing threshold. All models use an 80/20 random split, five regression metrics (RMSE, MAE, $R^2$, MSE, PRMSE), and five random seeds. GBT wins on SOC with $R^2=0.9943\pm 0.0014$, MAE $=0.25\%$, and $3.8\mu$s per-sample inference on a workstation CPU; this latency leaves headroom for on-board mission planning. Battery temperature and voltage together carry over $90\%$ of the predictive signal. GBT wins again on RUL ($R^2=0.596\pm 0.042$, MAE $=583$ s). The same ordering (tree ensemble ≻ recurrent ≻ linear) holds for both tasks; the remaining RUL gap reflects the single-flight dataset. The SOC labels originate from the on-board autopilot’s Coulomb-counting-based fuel-gauge estimator, so the SOC numbers should be read as a reproduction of that on-board trace at sub-microsecond inference latency rather than as independent accuracy; the calibration-free Coulomb-counting baseline reaches a marginally higher $R^2$ ($0.9950$, MAE $=0.35\%$) on the same task. Full article

This paper presents a bilevel Reinforcement Learning (RL) framework for optimizing Electric Vehicle (EV) charging through price-mediated coordination between grid operators and charging stations. Unlike prior work relying on direct control or manual subgoal engineering, the proposed approach uses dynamic pricing as an implicit coordination signal to address a complex multi-objective optimization problem involving grid stability, user satisfaction, and economic efficiency. To manage this complexity, the problem is decomposed into two levels comprised of an upper-level Distribution System Operator (DSO) that determines dynamic pricing strategies, and multiple lower-level Load Aggregators (LAs) responsible for EV charging decisions at individual stations in response to these prices. This bilevel structure captures the leader–follower interaction between DSOs and LAs, with each level operating at different temporal scales. Deep Deterministic Policy Gradient (DDPG) agents are deployed at both levels, enabling adaptive decision-making under operational constraints. Extensive simulations compare the framework against multiple Rule-Based Control (RBC) baselines. Results demonstrate that the DDPG-based DSO achieves a 42.4% higher mean reward and 19.1% higher profit compared to the best-performing RBC baseline, while preserving grid stability and user satisfaction. These results validate the effectiveness of bilevel RL for complex energy optimization problems, highlighting its potential as a scalable control paradigm for smart management systems. Full article

This study presents the design, manufacturing, and prototype-level evaluation of a high-efficiency interior permanent magnet synchronous motor (IPMSM) developed for compact electric vehicle traction applications. The proposed motor employs a 12-slot/10-pole spoke-type rotor topology and was evaluated in terms of electromagnetic performance, mechanical integrity, and thermal behavior. The slot–pole and winding configuration was assessed as part of the design evaluation, and the manufactured prototype was experimentally tested under different operating conditions. The experimental results were compared with numerical simulations using line-to-line back-EMF, efficiency maps, phase current–torque characteristics, and output power variation. At the nominal operating point of 7000 rpm and 3.5 Nm, the prototype delivered 2.5 kW output power with an experimental efficiency of 90.7%. The deviations between experimental and simulation results were 1.17% for phase current, 0.48% for line-to-line back-EMF, 1.18% for input power, and 1.20% for efficiency. Mechanical static structural finite element analysis indicated a rotor safety factor of 3.61 under the maximum centrifugal loading condition, while the resulting structural deformation remained sufficiently low to avoid adverse effects on air-gap alignment. In addition, the rotor incorporated an adhesive-free, mechanically disassemblable magnet-retention structure, which was mechanically evaluated under centrifugal loading and showed no magnet displacement, structural damage, or bolt-preload loss after testing. Thermal analysis and continuous-load experimental testing showed that the winding temperature remained around 80 °C under passive cooling conditions. Overall, the results demonstrate that the manufactured IPMSM prototype provides consistent electromagnetic performance, adequate mechanical reliability, and thermally safe operation for compact electric vehicle applications. Full article

With the growing demand for electricity, the penetration of Renewable Distributed Generators (RDGs), alongside the transition from Internal Combustion Engine Vehicles (ICEVs) to Electric Vehicles (EVs), has become a pressing challenge for the stable and efficient operation of distribution networks. This research focuses on a critical task of determining the optimal integration of RDGs, including solar photovoltaic systems, wind turbines, biomass units, and EV charging stations, into an Unbalanced Radial Distribution System (URDS). This work proposes an optimization approach aiming to minimise the total costs (TCs), active power losses (APLs), voltage unbalance factor (VUF), and voltage deviation (VD) of the network under consideration simultaneously. The integration of RDGs is carried out using a metaheuristic technique, which accounts for the intermittent nature of renewable energy sources, the stochastic behaviour of EVs, and the variability of load demands over 24 h a day. Fuzzy decision-making is applied to select an optimal trade-off solution from the Pareto front. The effectiveness of the developed approach is assessed comprehensively on a Pakistani 60-bus URDS as a primary study, while the IEEE-123 bus system is employed as a validation case to demonstrate the applicability and scalability of the proposed methodology. Among the five analysed case studies, the simulation results indicate that coordinated integration of RDGs and EVCSs into the system yields significant benefits, including a decreased reliance on conventional centralised generation, with a reduction of 56.29% in costs, 46.61% in losses, 7.17% in voltage unbalance, and 27.13% in voltage deviation as compared to the base case. Full article

Traditional location models for Park-and-Ride (P&R) facilities are constrained by the Independent and Identically Distributed (IID) assumption, failing to simultaneously capture inter-modal substitution elasticity and spatial path overlap, which leads to systematic demand forecasting biases. To address this gap, this study proposes an integrated Cross-Nested Logit (CNL) and Paired Combinatorial Logit (PCL) behavioral kernel within a bi-level programming framework, where the upper level minimizes total system generalized cost and the lower level simulates multi-modal Stochastic User Equilibrium (SUE). A hybrid GA-MSA solution strategy is developed. Experiments on the classic Sioux Falls benchmark network demonstrate that the proposed model identifies the optimal construction scale (N = 3) and the critical parking fee threshold (65 CNY) for mode shift. Compared to the un-nested MNL-PCL formulation, the integrated CNL-PCL framework provides an 11.75% downward behavioral correction in P&R market-share estimation, effectively counteracting the overestimation tendency inherent in conventional architectures. The optimal spatial layout (Nodes 4, 6, and 19) achieves a 54.40% share for “P&R + Public Transport” green modes and yields an annual net CO₂ mitigation of 957 tons. These findings confirm that synergistically characterizing mode correlation and path overlap provides a more prudent foundation for sustainable P&R planning. The proposed framework is also generalizable to other multi-modal facility location problems, such as transit-oriented hub sizing or electric vehicle charging network planning. Full article

The rapid expansion of lithium-ion battery (LIB) use in electric vehicles and large-scale energy storage systems has intensified the need for sustainable end-of-life management. While most research and industrial efforts have focused on recovering valuable metals, graphite anodes, despite constituting a significant portion of battery mass, remain relatively overlooked. This review evaluates current progress in graphite anode recycling, emphasizing technical challenges, scalability, and economic and geopolitical considerations. Conventional recycling methods, including hydrometallurgical, pyrometallurgical, and direct recycling processes, offer viable routes for material recovery but are often constrained by high energy demands, chemical consumption, and degradation of graphite quality. Regenerated graphite exhibits competitive electrochemical performance, with initial Coulombic efficiencies above 90% and reversible capacities comparable to those of commercial materials. In addition, strategies such as surface modification and defect engineering have proven effective in restoring structural integrity and enhancing cycling stability. Despite these advances, major challenges persist in achieving cost-effective, large-scale implementation and consistent material quality suitable for reuse in battery manufacturing. Given increasing supply risks and rapidly rising global demand for graphite, advancing sustainable recycling technologies has become essential. This review emphasizes the need for integrated technological innovation and supportive policy frameworks to enable the development of a circular economy for graphite. Full article

MagnetoRheological Brakes (MRBs) can provide wear-free, electrically controllable braking torque, but repeated high-load braking can cause rapid heat accumulation in the narrow rotor–stator gap and degrade MRF performance. This study evaluates rotor-only, stator-only and combined rotor–stator liquid-cooling configurations using transient 3-D conjugate heat-transfer CFD in ANSYS Fluent 2024 R1 for a UN Regulation No. 13-H-based 10-cycle duty profile (8.5 s acceleration, 20 s constant speed and 2.5 s braking per cycle). The activated MRF is modeled as an incompressible laminar Herschel–Bulkley fluid during braking, while the field-OFF phases use a Newtonian viscosity of 0.114 Pa·s; viscous dissipation and coil volumetric heating are included as internal heat sources. Cooling simulations apply water with a 130 kPa (absolute) inlet pressure and a conservative +20% heat-load margin with adiabatic external boundaries. Baseline uncooled dynamometer data (no integrated cooling) verify the thermal implementation, with a 7.06% underprediction of the measured temperature rise. In the uncooled case, the MRF reaches a temperature of 501 K after ten cycles; rotor-only and stator-only cooling reduce temperatures but do not fully suppress cumulative heating, whereas the combined configuration maintains the MRF below 400 K after ten cycles. These results indicate that cooling both dominant heat paths is required for stable MRB thermal operation under severe repeated braking. Full article

Interest in electric vehicles (EVs) is rising as the world shifts toward sustainable transportation, yet consumer adoption remains highly uneven, particularly in developing countries. This study examines how five dimensions of consumption value—functional, social, emotional, novelty, and conditional—influence consumers’ purchase intention toward EVs in Indonesia, while also testing the moderating role of infrastructure readiness. Using a quantitative approach, data were collected through an online survey with purposive sampling, yielding 455 valid responses. Partial least squares structural equation modeling (PLS-SEM) was applied to assess the measurement and structural models. The results reveal that functional, social, emotional, and conditional values significantly influence consumers’ purchase intention toward EVs, whereas novelty value has no significant effect. Infrastructure readiness also significantly moderates most consumption values, with negative coefficients indicating that limited charging access and inadequate maintenance support weaken the positive impact of consumer values on EV adoption. The findings show that although consumers value performance, social image, emotional appeal, and situational factors, poor charging infrastructure hinders purchase intention toward EVs. This study contributes to EV adoption literature by integrating consumption value theory with infrastructure readiness as a moderator. The results emphasize that developing charging infrastructure, expanding service availability, and maintaining supportive government policies are critical steps for accelerating EV adoption in emerging markets. Full article

The rapid expansion of Unmanned Aerial Vehicle (UAV) applications in both civilian and military sectors has intensified the demand for propulsion systems capable of delivering higher speed, increased endurance, and improved payload capacity. While battery-electric propulsion remains dominant for small UAV platforms, its limited energy density restricts operational range and mission flexibility. As a result, micro gas engines have emerged as a viable alternative for applications requiring high power-to-weight ratios and sustained high-speed operation. This review examines the fundamentals, scaling effects, and classification of micro gas turbine propulsion systems used in UAV applications, with emphasis on micro turbojets and related hybrid configurations. The paper discusses the thermodynamic principles governing micro gas engines and analyzes the aerodynamic, thermal, and combustion challenges associated with miniaturization, including low Reynolds number effects, tip leakage losses, thermal management limitations, and combustion instability. Furthermore, the study reviews the operational characteristics and mission suitability of different propulsion architectures for reconnaissance UAVs, high-speed UAVs, including reconnaissance and loitering platforms, target drones, and hybrid-electric aerial platforms. Recent developments involving additive manufacturing, advanced control systems, recuperated cycles, and hybrid-electric integration are also evaluated as enabling technologies for next-generation UAV propulsion. The findings demonstrate that although micro gas turbines continue to face important efficiency and manufacturing challenges at reduced scales, they remain essential for mission profiles that exceed the capabilities of purely electric propulsion systems. Full article

Range estimation for electric vehicles (EVs) is critical for intelligent transportation systems since it directly affects charging planning, route optimization, driver confidence, energy management, battery utilization, and driver decision-making processes. However, current studies still suffer from issues such as limited accuracy, insufficient interpretability, high computational complexity, dependence on simulation environments, or insufficient generalization capability under dynamic driving conditions. To address these limitations, this paper proposes an M5Boost framework that successfully integrates an additive residual learning methodology with the model tree structure. Unlike conventional boosting approaches, M5Boost combines iterative residual-driven learning, multivariate leaf regression models, tailored tree pruning, and specific smoothing mechanisms to improve prediction accuracy, robustness, and generalization capability for EV range estimation. A benchmark dataset was further systematically extended with newly collected real-world battery-related driving records. Experimental validation showed that the developed model significantly outperformed state-of-the-art models reported in the literature on the same dataset. Full article

The rapid growth of electric vehicle (EV) adoption has increased the importance of understanding charging behavior and improving the operational sustainability of charging infrastructure. Although existing studies have extensively investigated charging demand forecasting, charging load prediction, and charging behavior analysis, limited attention has been given to evaluating the sustainability of individual charging sessions. To address this gap, this study proposes a Sustainable Charging Session Index (SCSI) framework for assessing and classifying real-world EV charging behaviors based on operational charging characteristics. The proposed framework integrates the Entropy Weight Method (EWM), K-Means clustering, Principal Component Analysis (PCA), Random Forest feature importance analysis, and statistical validation techniques. A real-world dataset comprising 1929 EV charging sessions was analyzed, from which 1795 valid charging records were retained after preprocessing. Charging energy usage, average output power, and charging duration were selected as complementary indicators representing energy delivery effectiveness, charging efficiency, and temporal efficiency, respectively. The EWM assigned the highest weights to charging energy usage (0.5119) and average output power (0.4340), reflecting their greater discriminatory capability within the analyzed dataset. Clustering analysis identified three charging behavior archetypes, namely High-Sustainability Charging Sessions, Low-Sustainability Charging Sessions, and Efficient Charging Sessions. PCA demonstrated clear cluster separation, with the first two principal components explaining 97.9% of the total variance. Statistical analyses confirmed significant differences among the identified charging behavior groups (p < 0.001), while one-way ANOVA demonstrated strong internal consistency between the charging behavior clusters and SCSI scores (η² = 0.730). Furthermore, Random Forest analysis identified charging power as the most influential factor in differentiating charging behaviors. The proposed SCSI framework provides an objective and data-driven approach for charging session sustainability assessment, charging behavior characterization, and sustainable charging infrastructure management. Full article

Electric vehicle (EV) powertrains involve complex interactions between speed regulation, energy consumption, regenerative braking, and battery thermal behavior. Most existing approaches address controller tuning and energy management separately, which may limit the overall system performance. This paper proposes an integrated deep reinforcement learning (DRL) strategy in which a single Twin Delayed Deep Deterministic Policy Gradient (TD3) agent simultaneously adjusts the proportional and integral gains of the speed controller (K_pv, K_iv), the torque modulation coefficient (K_s), and the regenerative braking factor (β_reg). A multi-objective reward formulation is adopted to account for speed tracking performance, energy efficiency, regenerative energy recovery, battery thermal constraints, and driving comfort. The framework is implemented through a MATLAB R2022b/Simulink–Python 3.10 co-simulation environment that enables online interaction between the EV model and the learning agent. Performance is evaluated using the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). Compared with a conventional fixed-gain PI controller, the approach reduces gross energy consumption by 16.2%, decreases speed tracking error by 43.7%, increases regenerative energy recovery by 21.4%, limits battery temperature rise by 30.4%, and lowers RMS jerk by 33.7%. The results indicate that jointly optimizing control and energy management variables can improve both vehicle dynamic performance and energy utilization. The methodology offers a practical framework for the development of adaptive and intelligent control systems in future electric vehicles. Full article

The growing adoption of hybrid and plug-in electric vehicles increases heat generation in power electronic modules, driving demand for effective thermal management materials and reliable Al/Cu joining methods. However, large-area Al/Cu joints are challenging as conventional brazing requires high temperatures and flux, and fusion welding performs poorly with dissimilar metals. Contact-Reaction Brazing (CRB), which relies on eutectic-phase formation during heating, presents a promising alternative. Direct CRB of AA6063 and Cu might lead to severe aluminum dissolution above 570 °C. To mitigate this, large-area CRB of AA6063/Cu using a 4104 aluminum-foil interlayer is examined. Brazing temperature, holding time, and pressure are systematically varied to evaluate their influence on joint formation. Interfacial microstructures are characterized by SEM and XRD. Shear testing is used to assess fracture behavior and mechanical performance. A satisfactory shear strength of 48.8 MPa is achieved for the AA6063/AA4104/Cu joint under a brazing temperature of 540 °C, a holding time of 10 min, and an applied pressure of 600 Pa. Full article