World Electr. Veh. J., Volume 17, Issue 8 (August 2026) – 53 articles

The rapid advancement of autonomous driving technologies makes the effective coordination of vehicle groups a critical requirement for ensuring both safety and efficiency in smart urban environments. Although individual autonomous vehicles may operate correctly in isolation, their collective behavior can still lead to emergent issues such as deadlocks or collisions arising from complex inter-vehicle interactions. To address this challenge, we propose a hybrid automaton-based control framework for autonomous vehicle groups that integrates both normal and emergency operational modes to jointly guarantee safety and performance. In this paper, we present the formal specification and verification of the proposed system using rewriting logic and the Maude tool. Our main contributions are threefold: (1) the construction of detailed hybrid automata models that capture vehicle dynamics and decision-making; (2) the development of formal specifications in Maude from these models; and (3) the systematic verification of key system properties, including core safety invariants, such as collision avoidance, obstacle stopping, and velocity bounds. The verification results demonstrate that the proposed model consistently upholds safety conditions, ensures that vehicles come to a safe stop before encountering obstacles, and effectively prevents collisions within the group. Full article

A transition towards sustainable transportation requires efficient integration of electric vehicles (EVs) with renewable energy sources. This work proposes a two-stage Parking Lot Energy Management Scheme (PLEMS) to minimize charging costs while maximizing solar photovoltaic utilization in commercial parking facilities. In the first stage, the optimization phase is formulated using a mixed-integer linear programming (MILP) that minimizes the overall cost of EV charging while ensuring maximum utilization of locally available PV energy. In the second stage, a gated recurrent unit (GRU)-based deep learning model performs state of charge (SOC) forecasting for EVs parked in the parking lot. Using the predicted SOC for the next time step, the system decides whether each EV will be charged or discharged, ensuring consistency with the cost-optimal MILP strategy from the first stage. The proposed PLEMS achieves up to 62% daily cost savings in charging compared to uncoordinated direct grid charging. However, this cost saving is the outcome of proposed optimization as well as the integration of PV panels in power grid. When compared with nine similar vehicles to grid (V2G)-enabled approaches from the literature, which report cost savings ranging from 9.73% to 52%, the proposed framework shows an improvement of 10% to 52% over these methods. This hybrid MILP-GRU framework offers practical V2G operation and high scalability for large EV fleets in solar-powered smart parking lots. Full article

Electric motorcycles are central to Sub-Saharan Africa’s transition to electric mobility, yet manufacturers in the region typically rely on costly and time-consuming physical prototyping to optimise powertrains built from imported components. This paper presents a validated full-powertrain digital twin of the Roam Air, an electric motorcycle assembled in Nairobi, Kenya, developed in MATLAB/Simulink as four interconnected subsystems: the battery, the controller, the motor, and the vehicle dynamics. The battery is modelled as a Thévenin equivalent circuit whose parameters were experimentally derived at the pack level through Hybrid Pulse Power Characterisation tests, and the controller replicates the motorcycle’s field-oriented control with a maximum torque per ampere strategy, including its battery current and voltage limiting behaviour. The motorcycle’s regenerative braking characteristics, drag coefficient, and rolling resistance coefficient were experimentally obtained through braking, coasting, and coast-down tests. The digital twin ingests rider inputs and environmental information, and it predicts the motor’s speed and the battery’s power. Validation against six measured drive cycles in Stellenbosch, South Africa, demonstrates high correlation between predicted and measured profiles, with Pearson’s r values of 0.905–0.981 for battery power and 0.912–0.996 for motor speed, and energy consumption predicted to within 2.71% for five of the six trips. The presented modelling and characterisation framework offers manufacturers a transferable, computationally efficient alternative to iterative physical prototyping for powertrain optimisation. Full article

Distributed-drive electric vehicles (DDEVs) enable independent wheel-torque control, providing flexibility to improve energy efficiency and vehicle stability. However, tire–road adhesion, motor and battery capabilities, and actuator availability constrain these objectives, which may conflict under low-adhesion conditions, high-power acceleration, emergency braking, and combined longitudinal–lateral maneuvers. This paper provides a structured review of coordinated torque-allocation strategies for balancing energy efficiency and stability in DDEVs. Existing research is examined in terms of regenerative braking, tire-slip energy-loss reduction, and stability control under longitudinal, yaw, and combined conditions. Control approaches are classified as rule-based, stability-region-based, mode-switching, multi-objective optimization and predictive control, state-adaptive dynamic-priority coordination, and learning-based safety-hybrid methods. These approaches differ in real-time performance, constraint handling, adaptability, interpretability, and engineering maturity. A hierarchical hybrid architecture integrating rule-based supervision, state assessment, constraint-aware optimization, and learning-based enhancement appears more suitable for practical deployment than a single algorithm or fixed-weighting scheme. Key challenges include dynamic stability-boundary estimation, safety-assured coordination, multi-actuator fault tolerance, real-time implementation, and standardized vehicle-level validation. This review provides guidance for coordinated control-system development and future research on DDEVs. Full article

There is a growing trend toward employing two-coil and three-coil systems in magnetically coupled resonance wireless power-transfer (MCR-WPT) technology. However, these configurations have limitations at longer transmission distances and are sensitive to load variations. This paper provides a comprehensive review of four-coil WPT systems, focusing on their design, optimization, and applications. The reviewed literature indicates that four-coil configurations generally maintain higher power-transfer efficiency (PTE) over longer transmission distances, exhibit greater tolerance to misalignment, and show reduced sensitivity to load variations. The literature review indicates that while symmetric designs are easier to analyze, asymmetric configurations may provide higher efficiency and extended transmission range. The review also discusses how four-coil technology can be used in medical implants, sensors, and consumer electronics. It also addresses the current challenges of power and compliance in the electric vehicle (EV) industry. Finally, this paper identifies important areas of research that need to be addressed. Future research should aim to optimize power and efficiency together, explore multi-receiver systems for public charging, and investigate the potential of four-coil technology for Electric Micromobility Vehicles (EMVs). Full article

Conventional flux-reversal permanent magnet motors have problems such as excessive torque ripple and rich harmonic content in direct drive applications such as oil exploration, which restrict their application in high-precision scenarios. To address this issue, this paper presents a hybrid excitation topology that integrates double-armature windings, stator Halbach hybrid permanent magnet arrays, rotor-staggered unequal-tooth and rotor-hybrid permanent magnets. Two-dimensional finite element analysis was conducted using ANSYS Maxwell 2023 R1 to evaluate electromagnetic performance under rated steady-state conditions, rated power 300 kW, rated speed 83 rpm, rated voltage 660 V, rated phase current 307 A, and axial core length 200 mm. The simulation results show that the proposed topology has an average output torque of 34.5 kN·m at rated conditions compared with the traditional flux-to-reverse permanent magnet motor of the same size, and the torque ripple rate is reduced from 27.5% to 17.4%, a relative reduction of 36.8%. The results are based only on numerical simulation and have not been verified by physical prototype experiments. Dynamic control strategies, multi-load transient responses and experimental verification will be carried out in subsequent work. Full article

This paper investigates the multifaceted macroeconomic drivers of vehicle electrification in India and correlates them with micro-level corporate financial returns using a rigorous dual-stage econometric framework. Stage 1 employs a Newey–West time-series estimator on monthly observations to evaluate aggregate consumer demand elasticities across the automotive sector. Stage 2 utilizes a fixed effects panel specification with clustered standard errors across 10 major Indian automotive manufacturers over a four-year fiscal horizon. Stage 1 results demonstrate that short-run variations in Brent crude prices lack joint predictive power over domestic retail metrics ($F=0.89,p=0.4166$), supporting the thesis that state-owned OMC price-smoothing insulates short-term market dynamics from global oil shocks. Conversely, Stage 2 panel estimations prove that annual global Brent crude fluctuations yield no significant contemporaneous margin shocks. However, expanding annual EV market penetration exerts a substantive negative impact ($\beta =-2.49,p=0.107$) bordering statistical significance on corporate operating profit margins. This operational decoupling reflects a prominent industry ‘J-curve’, where accelerating consumer adoption cycles are countered by heavy front-loaded capital expenditures, asset re-tooling, and unoptimized economies of scale. These findings provide critical direct and indirect strategic insights for organizational stakeholders and policymakers navigating transitional capital cycles in emerging markets. Full article

This paper presents the development of a multi-domain simulation for the Porsche Taycan’s all-wheel-drive (AWD) electric propulsion system to investigate the impact of the rear drive unit’s two-speed transmission on performance and drive quality during maximum acceleration. This study was undertaken independent of the vehicle and propulsion system OEM. A lumped-parameter model of the front and rear electric drive units (EDU) and the high-voltage battery was developed and calibrated against the published data for key benchmarks, including 0–100 kph acceleration times and peak longitudinal acceleration. The mechanical shifting mechanism was reverse-engineered to simulate high-performance shift trajectories. To manage the transition, a clutch control scheme integrates a reduced-order clutch-to-clutch model featuring a feedforward (FF) torque estimator and a closed-loop feedback (FB) controller to achieve target input shaft speeds and shift durations. The study concludes with a comprehensive analysis of the propulsion system’s behavior at a battery state of charge of 96% and 25% and three electric motor speeds at which the upshift is commanded. The simulation results demonstrate that executing an early upshift at 10,700 rpm with 96% of SOC yields a 0.100-s inertia phase shift time, restricts the clutch thermal dissipation to 21 kJ, and achieves an 8-s velocity of 203.4 kph, outperforming the upshift at 15,300 rpm (0.210 s, 34 kJ, and 202.8 kph). Furthermore, the transient regenerative braking on the rear axle during the inertia phase reduces the peak current draw from 675 A to 87 A, recovering the DC bus voltage to enable cross-axle torque boosting on the front axle. Full article

Saudi Arabia’s transition towards electric mobility requires scalable charging infrastructure that can reduce dependence on grid-supplied energy and support national sustainability objectives. This study presents a preliminary planning assessment of petrol-station rooftops in Riyadh and Jeddah for photovoltaic (PV)-supported electric vehicle charging. Gross rooftop areas were measured for an exploratory, non-probability sample of 30 petrol stations—20 in Riyadh and 10 in Jeddah—using Google Earth Pro. A retained aggregate PVsyst output was used as a historical technical reference, while HelioScope was used only for preliminary layout visualisation. The retained 5.968 GWh/year PVsyst result cannot be independently reproduced from the materials currently available to the authors because the complete station-level project files and meteorological inputs were not preserved. A low–central–high scenario framework accounts for usable rooftop fractions of 70–90%, irradiance multipliers and combined operational-retention assumptions. The assumption-based scenarios produce 3.572–5.640 GWh/year, with a central value of 4.536 GWh/year. The 25, 40, and 60 kWh charging cases are defined as battery-side energy delivered per session and, after applying a 95% charging efficiency, correspond to approximately 71,800–172,400 energy-equivalent sessions annually under the central scenario. The PV-only economic screening gives an LCOE of USD 0.053–0.113/kWh and a simple payback period of 6.8–19.0 years under a full-self-consumption assumption; it excludes chargers, battery storage, structural reinforcement, and grid upgrades. The findings support further field investigation but do not establish complete EV-charging-infrastructure feasibility, national potential, or grid-independent operation. Full article

The rapid adoption of electric vehicles, together with increased battery mass and altered load distribution, is placing greater demands on ride comfort and suspension adaptability, while controller optimization may still request forces beyond the instantaneous capability of the physical semi-active actuator if experimentally supported force limits are not explicitly enforced. This study proposes a unified experimentally constrained optimization framework for a magnetorheological damper (MRD)-based semi-active suspension system using a two-degree-of-freedom quarter-car model. The damper is characterized at eleven current levels and represented by a branch-dependent lookup model that provides the zero-current baseline and instantaneous feasible force range. Proportional–integral–derivative (PID) and linear quadratic regulator (LQR) controllers are independently tuned using a genetic algorithm (GA) and particle swarm optimization (PSO) under identical vehicle dynamics, objective functions, tuning excitation, and MRD force constraints. Each candidate force demand is projected onto the experimentally derived feasible range throughout optimization. The controllers are tuned on a composite B–C–D profile and subsequently evaluated over nine road–speed scenarios. PID-PSO reduces the RMS sprung-mass acceleration by 15.91% and achieves the best acceleration performance in six cases, whereas LQR-PSO provides more balanced improvements in body motion, suspension travel, tire response, and force feasibility. The proposed framework therefore provides a more physically constrained basis for the comparative design and evaluation of MRD-based semi-active suspension control. Full article

At highway merging sections, merging vehicles must enter the mainline traffic flow within a limited acceleration section. Under high traffic demand, this process often involves rapid acceleration or deceleration. Conventional merging assistance control has mainly focused on reducing acceleration and ensuring safe inter-vehicle gaps. However, when electric vehicles (EVs) are considered, battery energy consumption is also an important evaluation perspective. This study evaluates the effects of different speed adjustment strategies for merging vehicles on EV battery energy consumption and smooth merging performance at a single highway merging section. Four cases are compared: Acceleration-Minimizing Merging Control (AMC), which minimizes the absolute value of the required acceleration; Fixed-Arrival-Time Energy-Minimizing Merging Control (FEMC), which minimizes battery energy consumption under a fixed arrival time; Variable-Arrival-Time Energy-Minimizing Merging Control (VEMC), which minimizes battery energy consumption without fixing the arrival time; and a no-control case. EV battery energy consumption is calculated by integrating battery-side power over time, considering driving resistance, inertial force, drivetrain efficiency, regenerative braking efficiency, maximum regenerative power, and auxiliary power. The simulation results show that AMC is advantageous in terms of smooth merging performance, whereas VEMC achieves the lowest overall average battery energy consumption. FEMC and VEMC reduced battery energy consumption by up to 10.7% and 39.5%, respectively, compared with AMC under the evaluated initial-speed conditions, although their smooth merging performance decreased under some conditions; however, their smooth merging performance remains lower than that of AMC, and the energy-saving effect depends on the initial speed and traffic demand conditions. These results indicate that EV-oriented merging assistance control requires a control design that considers the trade-off between energy efficiency and smooth merging performance. Full article

Proton exchange membrane fuel cell (PEMFC) systems typically generate low-voltage and high-current DC power, requiring a step-up converter interface for electric vehicle (EV) and DC microgrid applications. In addition, excessive current ripple and rapid transient loading conditions may increase electrical and thermal stress within the fuel cell stack. Consequently, both converter topology and control strategy play important roles in maintaining stable system operation and favorable PEMFC operating conditions. This paper presents a differential flatness-based nonlinear control strategy for a PEMFC-fed multiphase interleaved boost converter. The proposed control structure combines inner-loop inductor current regulation with outer-loop DC bus energy regulation. This configuration achieves stable voltage control, balanced phase-current sharing, and reduced fuel cell current ripple during transient operating conditions. A two-phase interleaved boost converter prototype was experimentally implemented using a 2.5 kW PEMFC platform and a dSPACE DS1202 MicroLabBox real-time controller. Experimental tests under steady-state and dynamic loading conditions were conducted to evaluate DC bus voltage regulation, transient response, current-sharing capability, and robustness against load disturbances. The experimental results demonstrated that the proposed nonlinear controller achieved faster transient voltage recovery and smaller DC bus voltage deviation compared with a conventional PI-based control approach. In addition, the interleaved converter structure reduced input current ripple at the PEMFC output terminals during dynamic operation. Overall, the results indicate that the proposed control strategy is suitable for PEMFC-powered EV and DC microgrid applications requiring stable DC bus regulation and fast dynamic power control. Experimental results demonstrate that the proposed controller reduces the DC bus voltage recovery time from approximately 150 ms to 50 ms, corresponding to a 66.7% improvement over a conventionally tuned PI controller. In addition, the maximum DC bus voltage deviation is reduced from approximately 1.0 V to 0.5 V while maintaining balanced phase-current sharing with less than 3% mismatch throughout the tested operating conditions. Full article

Brushless DC (BLDC) motors are now the dominant propulsion choice for electric vehicles (EVs) because of their high torque density, efficiency and reliability, but their nonlinear dynamics, electronic commutation, and wide load and speed range make fixed-gain control difficult. A single set of proportional–integral–derivative (PID) gains tuned at one operating point degrades when inertia, back-EMF, or load torque change. This paper presents a hybrid adaptive PID speed controller for a BLDC EV drive that couples an online PID auto-tuner that re-estimates the gains from a frequency response estimate of the plant, with a fast fixed-structure PID that supplies the rapid corrective action that the auto-tuner cannot provide during its estimation interval. The novelty of this work is this explicit two-element decomposition operating on a cascaded speed/voltage loop driven by Hall sensor feedback, which removes the need for an exact analytical feedback model while retaining the transparency of classical PID. A full analytical model of the BLDC machine and the closed-loop transfer functions is derived and implemented in MATLAB/Simulink. Across step references of 1000–1800 rpm and load steps to 10 N·m, and against a conventional fixed-gain PID and a Flower Pollination Algorithm (FPA)-tuned PID, the proposed controller holds overshoot below 1% at low-to-mid speed and a consistently lower torque ripple, while a 12.4% transient undershoot at 1800 rpm under sudden load identifies the present operating limit and a direction for future work. Full article

The decarbonisation of heavy-duty road transport is central to European climate policy, yet the economic viability of battery-electric trucks under real-world route conditions remains uncertain. This study presents a route-specific total cost of ownership (TCO) analysis comparing an 18-tonne battery-electric truck with a comparable diesel truck across four Danish distribution routes. An eight-year net present value (NPV) model at 5% discount rate captures acquisition, energy, taxation, and maintenance costs. The analysis incorporates seasonal variation and distinguishes between depot charging (0.75 DKK/kWh) and public fast charging (3.00 DKK/kWh). The electric truck achieves NPV advantages of DKK 1.01–2.14 million across all routes, with break-even periods of 1.8 to 3.3 years. Charging strategy is the most influential economic factor. The findings demonstrate that fleet-averaged TCO estimates are insufficient and route-level analysis is essential for informed electrification decisions, though results are contingent on the specific policy and operational context of this single-operator case study. Full article

To overcome the limitations of conventional model predictive control (MPC) for trajectory tracking of distributed-drive four-wheel-steering (4WS) vehicles, particularly its fixed weighting matrices and prediction and control horizons, this study investigates the integrated trajectory tracking and stability control of an automated distributed-drive electric vehicle equipped with four independently controlled in-wheel motors and a four-wheel-steering system. The main novelty of this study lies in the simultaneous online adaptation of the MPC weighting matrices and reconfiguration of the prediction and control horizons, together with the coordinated integration of four-wheel steering and direct yaw moment control (DYC) within a unified trajectory tracking framework. Unlike conventional adaptive MPC methods that primarily adjust weighting parameters, the proposed adaptive prediction and control horizon adjustment (APCHA) strategy jointly updates the prediction and control horizons according to the integrated tracking error, error variation rate, and control input variation rate. Meanwhile, a fuzzy adaptive weighting mechanism adjusts the MPC weighting matrices online. At the lower control layer, a torque allocation method considering both the tire load ratio and vertical tire loads is employed to realize the required direct yaw moment. Finally, CarSim–Simulink co-simulation is conducted to verify the effectiveness of the proposed control strategy. Simulation results demonstrate that, at a vehicle speed of 60 km/h and a road adhesion coefficient of $\mu =0.5$, the proposed Improved MPC-4WS controller reduces the maximum lateral tracking error by 34.9% compared with the conventional MPC-4WS controller, thereby demonstrating superior trajectory tracking performance. Furthermore, the ablation study verifies the effectiveness of the proposed hierarchical architecture by quantifying the contributions of the DYC module and the optimized torque allocation strategy. Full article

To address the insufficient control accuracy of traditional vehicle stability control methods under nonlinear conditions, this paper proposes a combined stability control strategy for distributed-drive electric vehicles based on the phase plane method. A two-degree-of-freedom vehicle dynamics model incorporating the Magic Formula tire model is established. The $\beta -\dot{\beta }$ phase plane is selected, and a dynamic stability boundary function is constructed through saddle point analysis and road adhesion coefficient fitting. An instability index is defined to quantify the deviation from the stable state. Based on this index, a hierarchical control strategy is designed: within the stable region, model predictive control (MPC) is employed for yaw moment optimization via differential torque distribution among the four in-wheel motors; when the vehicle enters the unstable region, sliding mode control-based active rear-wheel steering (ARS) is activated. The strategy is validated through CarSim-Simulink co-simulation under step steering and slalom maneuvers. Results show that under the high-speed step steering condition, compared with the uncontrolled case, the combined control reduces the peak yaw rate by 5.3%, the overshoot from 32.86% to 27.38%, the settling time from 9.87 s to 8.02 s, and the oscillation amplitude by 36.5%; under the slalom condition, the yaw rate amplitude is reduced by 5.4%. The proposed strategy effectively improves vehicle handling stability. Full article

Against the backdrop of intertwined economic advancement and ecological governance dilemmas confronting developing economies, this paper centers on the green growth objective embedded within China’s New Energy Vehicle Pilot (NEVP) Policy as its analytical focal point. Adopting the propensity score matching difference-in-differences (PSM-DID) framework, this study empirically evaluates the causal impacts of the NEVP policy on green economic development efficiency. The results indicate that the promotion of new energy vehicles yields a statistically significant improvement in green economic efficiency. Notably, the effect of the NEVP policy is more pronounced in cities with higher levels of economic development. Through mechanism analysis, we find that new energy vehicles play a crucial role in promoting green economic growth and sustainable development. Furthermore, this study also highlights the spatial effects of new energy vehicle promotion on green economic development. This research provides empirical evidence to guide the strategic promotion of new energy vehicles in developing regions to improve environmental quality and underscores the sustainable growth potential of aligning economic and environmental goals. Full article

Permanent magnet synchronous motors (PMSMs) are widely used in AC drive systems, and their control performance depends strongly on accurate motor parameters. Conventional proportional-integral model reference adaptive system (PI-MRAS) observers use fixed adaptation gains, resulting in a trade-off between rapid convergence and low steady-state fluctuation. To address this limitation, this paper proposes a fuzzy proportional integral (Fuzzy-PI)-tuned MRAS observer for the simultaneous online identification of stator resistance (Rs) and stator inductance (Ls). The parameter-error dynamics are formulated from the PMSM model, and the adaptation laws are derived using Popov hyperstability theory. A fuzzy tuner uses the absolute identification error and its rate of change to schedule the proportional and integral gains online, thereby accelerating transient error convergence when the identification error is large and reducing estimation oscillations during steady-state operation. The method is evaluated through simulation and laboratory experiments involving rated operation, speed variation, parameter perturbation, and load disturbance. Under the investigated conditions, the identification errors of Rs and Ls are 3.8% and 0.18%, respectively. Compared with the conventional PI-MRAS, the reported Rs identification error decreases from 8.1% to 3.8% and the Ls identification error decreases from 0.91% to 0.18%. The results demonstrate an improved identification accuracy and disturbance recovery within the tested operating range. The implementation on an Infineon TC233 platform also demonstrates real-time feasibility, while broader validation under temperature variation, magnetic saturation, inverter nonlinearity, and measurement noise remains necessary. Full article

To encourage a wild spread of EVs, accurate SoC and SoH estimation under thermally accelerated aging is critical for advanced BMS systems. This study presents a lifelong degradation analysis of LiBs cells over a 7700 cycle at 40 °C. An adaptive third-order ECM, coupled with a hybrid polynomial–logarithmic OCV formulation, is continuously identified via PSO. The experimental data reveals a distinct degradation profile where the cell crosses the 80% SoH EoL threshold at cycle 5500, steadily declining to a terminal state of 75.7% SoH. Continuous parameter tracking isolates key electrochemical transitions: an initial kinetic stabilization phase is followed by a synchronized thermodynamic OCV realignment near cycle 2000, consistent with increasing LLI. Mid-life aging features a pronounced increase in the time constants, while a late-life degradation is characterized by increasing transport limitations, reflected in the evolution of the slow diffusion-related model parameters, inducing parameter boundary clipping in the slow diffusion branch. Despite these physical non-linearities, the proposed framework maintains high global fidelity throughout the 7700-cycle lifespan, strictly bounding the SoC RMSE below 2.5%, keeping Voltage RMSE under 35 mV, and preserving an R² above 0.970. Comparative evaluations indicate that the proposed framework achieves a favorable balance between computational efficiency, tracking accuracy, and long-term diagnostic stability. Full article

This study quantifies whether vehicle electrification reduces greenhouse gas emissions on one of the world’s most fossil-intensive electricity grids. A transparent, ISO 14040/14044-conformant cradle-to-grave life cycle assessment compares a mid-size battery electric vehicle (BEV, 60 kWh) with a comparable gasoline car over 225,000 km, using a fully source-traceable process-sum inventory and life cycle (well-to-wheel) emission factors for both energy carriers. On the 2024 Saudi grid (692 g CO₂e/kWh, 99.8% fossil) the BEV emits 37.8 t CO₂e (168 g CO₂e/km) against the gasoline car’s 50.6 t (225 g CO₂e/km)—a 25% reduction, with the BEV’s 1.9 times higher production emissions repaid at 76,000 km, approximately three years of typical Saudi driving. The advantage rises to 44% on the world-average grid, 53% under Saudi Arabia’s 50% renewable-electricity target for 2030, and 66–80% on the EU and French grids; grid parity would require 991 g CO₂e/kWh, above any national grid. The result is robust to hot climate energy consumption (+15%, advantage 25%), Gulf-sourced materials (break-even shortens to 68,000 km), battery capacity (40–80 kWh), and 10,000-run Monte Carlo uncertainty propagation (BEV superior in 99.6% of draws). Electrification is therefore a sound climate strategy even in fossil-grid economies, and its benefit roughly doubles with the announced power-sector transition. Full article

Believing that electric vehicles (EVs) benefit the environment may be associated with EV intentions, but current car owners and non-car owners answer different practical questions. This cross-sectional online survey of 328 adults residing in Canada therefore analyzes the groups separately. The focal item—“The use of EVs will help protect the environment”—is treated as a perceived environmental benefit of EVs rather than as a validated general environmental-concern scale. For 226 car owners with complete focal variables, an ordered logistic model including personal environmental responsibility produced an odds ratio (OR) of 1.82 per one-category increase in perceived environmental benefit (95% confidence interval [CI] 1.51–2.19; p < 0.001). The association remained positive in the available demographic sensitivity model (OR 2.05, 95% CI 1.68–2.51). For 72 non-car owners, the parsimonious exploratory model produced an OR of 1.47 (95% CI 1.04–2.08; p = 0.029), but the estimate was attenuated after broader demographic adjustment (OR 1.39, 95% CI 0.96–2.02; p = 0.080). Cluster-robust stacked cumulative-logit diagnostics found no evidence against proportional odds in the primary models. Because owners reported five-category purchase likelihood for a plug-in electric vehicle and non-owners reported seven-category stated EV preference, no formal group comparison was conducted. The findings are associational, based on single items and a non-probability sample, and are consistent only with selected propositions of Value–Belief–Norm theory. Evidence is strongest for a positive owner–context association and suggestive, but less stable, for non-car owners. Full article

Uncoordinated charging and discharging of large-scale electric vehicles (EVs) exacerbates grid peak–valley fluctuations, while deep discharging accelerates battery degradation. To address these challenges, this study proposes a coordinated charging–discharging optimization strategy integrating dynamic discharge restraint and a three-dimensional weighted comprehensive objective covering electricity price signals, grid operational constraints and battery health state. First, an EV travel behavior model is established to characterize spatiotemporal availability. Subsequently, a coupled battery aging model is developed by combining a power-law-based cycle aging formulation with a square-root calendar aging model, based on which an adaptive linkage mechanism between the depth-of-discharge upper bound and a net-revenue threshold is introduced. Building on these components, this model is constructed to jointly optimize three sub-objectives: charging station revenue maximization, battery lifetime cost minimization, and load fluctuation suppression, thereby mitigating grid peak–valley differences while reducing battery degradation and discharge costs. Multi-scenario simulations demonstrate that the proposed strategy, by coupling discharge restraint with spatiotemporal dynamic pricing, enables precise peak shaving of discharge power. For a fleet of 50 EVs, the charging station revenue reaches 1073.7 CNY, the grid peak–valley difference is reduced by 9.8%, and the battery degradation cost decreases by 23.1% compared with conventional strategies, corresponding to a carbon emission reduction of 386.4 tCO₂. When scaled to 100 EVs, the revenue increases by 101.9%, while the peak–valley difference is further reduced by 0.6%, demonstrating the effectiveness of the proposed strategy in enhancing economic performance, extending battery lifetime, and supporting grid stability. Full article

An Adaptive-Parameter Maximum Correntropy Kalman Filter (APMCKF) algorithm is proposed to address state estimation degradation in semi-active suspensions caused by non-linear coupling between time-varying physical parameters and non-Gaussian noise. First, a time-varying dynamic model with non-linear damping is established via bench tests. A genetic algorithm (GA) globally optimizes key physical parameters to suppress model mismatch. Second, the APMCKF integrates an adaptive suspension parameter update mechanism. This closed-loop mechanism refreshes the system state matrix in real-time, effectively overcoming state-tracking lag. Concurrently, the maximum correntropy criterion (MCC) is embedded within the Sage–Husa recursive framework to dynamically reconstruct the observation noise covariance matrix, ensuring robust filtering under heavy-tailed noise. Simulations under ISO Class A–D random road profiles demonstrate that the APMCKF reduces the root-mean-square error (RMSE) by 62.33–81.24% compared to the adaptive Kalman filter (AKF). It also outperforms the adaptive-parameter Kalman filter (APKF), yielding a 27.49% accuracy improvement on Class D roads where non-Gaussian noise is most severe. Moreover, comparative evaluations against standard non-linear Bayesian filters demonstrate that the APMCKF successfully overcomes the truncation errors of the Extended Kalman Filter (EKF) and the tracking hysteresis of the Unscented Kalman Filter (UKF), reducing the average RMSE by up to 74.98% and 60.76%, respectively, under severe Class D non-Gaussian excitations. Furthermore, the algorithm exhibits excellent disturbance rejection under transient speed bump impacts and maintains stable error reduction across vehicle speeds of 10–25 m/s. Ultimately, the APMCKF delivers high-precision estimation and exceptional robust stability under variable speeds and non-Gaussian disturbances. Full article

Public-data electric-vehicle charging-station siting needs a screening workflow that can use spatial proxies while keeping demand, grid-capacity, and implementation claims separate from the score. This study develops an interaction-aware Nonlinear Interaction–Einstein Aggregation (NI-EA) framework for Dubai and extends it with source-conditioned robust candidate-set diagnostics. From 7410 admitted candidate/amenity records, 5097 inside-boundary candidates are scored using a candidate-derived activity-density proxy, a charger-coverage-gap proxy, and a grid-access proxy. The analysis compares NI-EA with WSM, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Einstein aggregation; reconstructs a 63-scenario interaction/curvature/blending rank matrix; evaluates weighting, road-network, and official-DEWA source sensitivities; and reports necessary and possible top-K candidate sets, family-balanced finite-scenario acceptability, rank-displacement summaries, and bounded spatial-evidence context from official community, transport, parking, DEWA, and OpenStreetMap-derived sources. The baseline leader is S1421/Boonmax, while official-DEWA coordinate-source reconciliation changes the leader to S3473. Across the reconstructed interaction, weighting, road-network, and official-DEWA scenario families, the top-15 necessary core contains 12 candidates, and the top-15 possible envelope contains 18 candidates. Activity-radius and charger-count coverage alternatives are reported separately as proxy-definition sensitivities. TOPSIS has 0/15 top-15 overlap with NI-EA because it favors a different profile with much higher coverage-gap scores but low activity density. The reported output is therefore a source-conditioned planning shortlist and robustness audit, not an observed-demand map, feeder-capacity validation, financial feasibility assessment, or construction recommendation. Full article

Silicone oil is a promising dielectric coolant for battery immersion cooling, yet the ability of commercially available conductivity sensors to detect battery-related contamination remains poorly quantified. In this study, simulated carbonaceous particles, electrolyte, and mixed solid–liquid contaminants were introduced into silicone oil under controlled conditions using a closed-loop circulation platform, and conductivity was monitored in real time. Pristine silicone oil exhibited a baseline conductivity near the instrumental detection limit (approximately 1.26 μS·cm⁻¹). No measurable conductivity increase was observed for particle concentrations up to 10 g·L⁻¹ or electrolyte additions up to 3.0 vol%. Only under an intentionally extreme condition involving 20 vol% electrolyte and vigorous mixing were transient conductivity spikes of 350–550 μS·cm⁻¹ detected. Thus, within the application-relevant concentration range examined, conductivity monitoring showed limited sensitivity to progressive contamination. These findings concern the response of a commercial low-field conductivity sensor and do not constitute a complete assessment of leakage current, dielectric strength, or full thermal-runaway conditions. Full article

To satisfy the constant-current/constant-voltage (CC/CV) charging requirements of electric-vehicle batteries, this paper investigates the extension of an established fixed-frequency reconfigurable three-coil wireless power transfer system using a modified receiver-side LCC-type network. The receiver-side shunt capacitor introduces a current-dividing path, so the receiver-coil current differs from the equivalent rectifier-load current and the strict self-resonant value of the L₂—C₂ branch no longer provides the intended CC regulation. A current-division-aware fundamental-harmonic model is established, and a closed-form correction gives C₂ = 21.950 nF instead of the strict resonant value of 22.825 nF at 85 kHz. Calculated results show that the maximum CC target error over RL = 15–35 Ω is reduced from 8.948% to 0.031%, while the calculated CC/CV transition resistance is restored from 39.25 Ω to 34.97 Ω. Comparative analytical results are provided for the series-compensated baseline, the strictly resonant LCC-type configuration, and the corrected LCC-type configuration, together with receiver-current and coupling-sensitivity assessments. At RL = 35 Ω, the retained prototype measurements give an output voltage of 109.14 V, an output current of 3.121 A, an output power of 0.341 kW, and a DC-DC efficiency of 93.120%. The prototype measurements confirm the nominal operating target of the corrected design, while the analytical and numerical results clarify the modeling, correction, and current-stress tradeoffs introduced by the modified receiver network. Full article

The increasing penetration of electric vehicles (EVs) introduces significant uncertainties into fast-charging station (FCS) planning due to the stochastic nature of EV charging behavior. Accurately representing these uncertainties is essential for making reliable planning decisions in coupled transportation–power networks. This paper proposes a copula-based stochastic planning framework for the optimal allocation of FCSs while accounting for the correlated uncertainties associated with EV charging behavior. A multivariate copula model is employed to capture the dependency structure among key charging variables and generate realistic stochastic charging scenarios, which are subsequently incorporated into the EV charging load forecasting process over the planning horizon. Based on the resulting stochastic charging demand, a multi-objective optimization model is developed to simultaneously minimize investment costs and EV users’ travel distances, improve distribution network performance, and maximize environmental benefits through decarbonization. In addition, distributed generation (DG) units are optimally integrated to improve voltage profiles and reduce power losses. The proposed framework is implemented using MATLAB R2013a and R.4.0.2 and evaluated using both the IEEE 33-bus test system and a realistic 37-bus coupled transportation–power network in Meshgin-Shahr, Iran. The results demonstrate the effectiveness of the proposed stochastic planning framework in addressing uncertainties in EV charging behavior and identifying robust FCS deployment strategies. Full article

The gap between consumers’ intent to buy electric cars and their actual purchasing behaviour of petrol cars has motivated researchers to conduct numerous investigations over the last decade. Nevertheless, with the explosive development of the field, bibliographic mapping remains a difficult task. This study combines bibliometric analysis and a PRISMA-oriented systematic review of consumer purchase intentions towards sustainable vehicles. A systematic literature search in Scopus retrieved 1447 records between 2015 and June 2026, of which 706 empirical studies met the inclusion criteria for qualitative synthesis. Based on performance analysis and science mapping using VOS viewer software, one can conclude that the topic area develops at around 23%, gathers 43,758 citations, and shifts geographically over time. China contributes 418 papers, and India goes from publishing one piece in 2015 to occupying the third position overall. Keywords co-occurrence identifies five research clusters, whereas co-citations show that there are three intellectual bases for the field: behavioural theory, choice modelling, and methodological approaches based on the PLS-SEM framework. The results also emphasize some recurring gaps, namely the lack of a moderating effect of demographic characteristics, personality traits, intention-to-behaviour relationship, and longitudinal surveys. This study proposes an eight-point research agenda driven by keyword analysis. Full article

Amid global energy demand escalation, renewable energy intermittency, and electric vehicle (EV) charging demand concentration exacerbating power grid supply–demand contradictions, Vehicle-to-Grid (V2G) emerges as a solution, yet EV users’ V2G acceptance and participation willingness lack in-depth exploration. This study aims to fill this research gap by investigating Chongqing EV users’ V2G acceptance, behavioral intention, and influencing mechanisms to provide support for V2G promotion. It targets EV owners in Chongqing’s downtown areas, collecting 295 valid questionnaires, covering users’ demographics, travel-charging habits, and subjective attitudes. Based on technology acceptance and usage theories, it constructs a structural equation model (SEM) with perceived usefulness, ease of use, economic viability, and technological risk as latent variables to analyze their impacts on behavioral intention. Results show that perceived usefulness, perceived ease of use, and economic benefits positively affect behavioral intention, while technology risk perception exerts a negative effect; users with fixed commutes, low-range anxiety, and home charging piles are more receptive, and 70% support V2G but worry about battery wear and plug-in duration. Its innovation lies in integrating EV charging–discharging and travel patterns into the analysis, and its findings enrich new energy technology acceptance theory and provide a theoretical basis for transportation-energy system coordinated planning and V2G development. Full article

New energy vehicles constitute a crucial component of low-carbon economic systems and green development initiatives. Supported by government subsidy policies, the new energy vehicle industry has achieved remarkable development in recent years. This study conducts an empirical analysis based on panel data of 93 listed new energy vehicle enterprises from 2012 to 2022 to explore the impacts of government subsidies on corporate R&D investment. Using Stata 17.0, we use return on assets, debt-to-asset ratio, enterprise size and operating efficiency as control variables. A two-way fixed-effect model is selected via the Hausman test, followed by linear regression analysis. Furthermore, a dynamic panel vector autoregression (PVAR) model is employed to examine the dynamic interaction between government subsidies and corporate R&D investment. This research perspective overcomes the limitations of traditional static innovation policy research, effectively supplements the empirical evidence on long-term policy incentive effects in the new energy vehicle industry, and enriches the theoretical and empirical literature on the intrinsic dynamic correlation between government subsidies and corporate innovation investment. The empirical results show that government subsidies exert a significantly positive effect on firms’ R&D investment, and that there exists a stable long-term two-way positive interaction and dynamic equilibrium between the two. However, such mutual promotion effects are economically weak in magnitude, and the long-term evolutionary trends of both variables are predominantly dominated by their respective internal self-reinforcing inertia. In view of the limited incentive contributions of existing subsidy policies, the results of this study suggest the need to optimize the precision and targeting of government subsidy mechanisms to amplify policy incentive efficiency, while enterprises should fully leverage their endogenous R&D inertia to strengthen their independent innovation capabilities. The presented findings provide empirical evidence and policy guidance for the promotion of stable R&D innovation and high-quality development of the new energy vehicle industry. Full article