Search Results (1,446)

In light of the advancement of vehicle electrification and intelligence, four-wheel independent drive (4WID) electric vehicles (EVs) have garnered significant attention as a promising platform. Integrating advanced torque-vectoring (TV) strategies into 4WID EVs can effectively optimize the synergistic performance between handling stability and energy efficiency of the over-actuated system across various driving conditions. In this paper, a hierarchical Combined Sliding Mode Control–Adaptive Nonlinear Model Predictive Control (cSMC-ANMPC) TV strategy is proposed to enhance the comprehensive performance of 4WID EVs and ensure adaptive control across diverse driving conditions. Firstly, a hierarchical control architecture is developed to decouple the complex multi-objective problem. The upper layer performs robust stability decision-making by observing the vehicle’s state errors. The lower layer determines the optimal torque distribution throughout the powertrain. Secondly, a Combined Sliding Mode Controller (cSMC) is developed for the upper layer to promptly generate a robust stability command. By co-regulating both yaw rate and sideslip angle into a single command, it simplifies the lower layer’s task and enhances overall stability. Thirdly, a Soft Actor-Critic (SAC) intelligent tuner is integrated into the lower-layer NMPC to mitigate the effects of varying conditions on the stability–economy trade-off and strengthen the adaptability of the controller. Finally, co-simulation evaluations on the MATLAB R2023b/CarSim 2020.0platform demonstrate that the proposed cSMC-ANMPC strategy can improve comprehensive performance for the studied 4WID EV. Compared with other baselines, the stability enhancement in extreme maneuvers and the long-term energy-saving capability are remarkable, showcasing its promising performance. 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

Transport decarbonization through electric mobility depends not only on how many charging stations are deployed but where, and whether expansion balances accessibility, grid readiness, land-use protection and regional equity. Türkiye, targeting net-zero by 2053 with electric car sales exceeding 10% of the market in 2024, shows a highly uneven charging network: provincial provision ranges from 9.0 to 155.6 points per 100,000 inhabitants, with the least-served half of the population holding only 22.3% of installed capacity (Gini = 0.311). This study develops a GIS-based multi-criteria framework treating charging expansion as a sustainability-constrained planning problem. Six criteria, namely population, GDP, transformer and transmission-line proximity, road-network proximity, and city-centre proximity, were harmonized to a 100-m grid via fuzzy membership functions, with an exclusion mask protecting sensitive land uses. Three weighting scenarios were compared: equal weights (EVCSI-A), Random Forest-derived weights (EVCSI-B), and expert AHP weights (EVCSI-C). Road accessibility (41.12%) and economic capacity (29.84%) dominated existing placement, explaining ~71% of feature importance, stable across algorithms and bootstrap replicates. National results reveal an efficiency–equity trade-off: EVCSI-B concentrates suitability in metropolitan corridors, EVCSI-A preserves broader coverage, and EVCSI-C reinforces metropolitan bias. Central and Eastern Anatolia remain underserved. We recommend sustainability-constrained screening followed by grid-capacity verification, positioning EVCSI as a transferable equity-monitoring tool supporting SDG 7, 9, 11 and 13. 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

This paper proposes an integrated control allocation method for a vehicle equipped with all-wheel drive (AWD) and an electronic limited-slip differential (eLSD). Unlike those of electric drive systems, the admissible clutch inputs of AWD and eLSD systems vary with drivetrain structure, engine torque, and vehicle operating conditions. To address this issue, a weighted least squares (WLS) control allocation framework is designed to coordinate the transfer clutch and the left/right eLSD clutches while considering physically derived input constraints. The proposed controller is evaluated using a CarSim–Simulink AWD-eLSD vehicle model under acceleration during steady-state cornering and double-lane-change maneuvers on high- and medium-friction road surfaces. The simulation results show that the proposed method improves yaw-rate tracking while maintaining small sideslip angles and bounded rear-wheel slip ratios compared with uncontrolled and maximum-eLSD-input cases. In addition, the proposed control allocation method requires substantially lower computation time than an MPC-based approach, supporting its suitability for real-time AWD and eLSD control applications. 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

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

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

To address the challenge of balancing junction temperature prediction accuracy and computational efficiency for high-power multi-chip IGBT modules in automotive applications during complex electro-thermal conversion processes, this study proposes an improved three-dimensional thermal network model based on equivalent power loss injection. Firstly, the effective heat conduction area of each packaging layer under actual heat flow distribution is extracted through three-dimensional finite element simulation, and the single-chip self-heating network parameters are constructed. Secondly, targeting the thermal cross-coupling effect among multiple chips, an elliptical thermal diffusion model is applied to accurately define the thermal coupling region, and a dynamic equivalent power loss compensation mechanism is introduced. Efficient decoupling of multi-heat-source interference is achieved without increasing the state-space dimension of the model. An experimental benchmarking results comparison indicates that the absolute error of junction temperature prediction by this model under steady-state operating conditions is 0.5 °C. Further comparative analysis under the full CLTC-P (China Light-duty Vehicle Test Cycle for Passenger Car) cycle verifies that the improved model not only overcomes the shortcomings of the traditional Foster model, which severely underestimates the transient peak junction temperature and alternating stress amplitude, but also effectively filters out non-physical overshoots caused by short-term ultra-narrow pulses, thus reasonably estimating the device’s maximum junction temperature within the real physical boundary. This method provides efficient theoretical support for accurate dynamic junction temperature predictions and reliability evaluations of electric vehicles under complex operating conditions. Full article

The electrification of road transportation has been widely proposed as a viable strategy for minimizing fossil fuel dependency and the environmental impacts of traditionally powered motor vehicles. This strategy has led to the increased adoption of electric vehicles (EVs), such as electric cars, electric buses (e-buses), electric trucks (e-trucks), etc., in cities, as they are more sustainable. Initiatives directed towards the electrification of vehicles can contribute towards sustainable transportation. One of these initiatives is the development of electric road systems (ERSs), which enable roadways to supply electric power to electric vehicles when the vehicles are in motion. The deployment of ERSs has developed as an alternative to address issues that negatively impact the adoption of EVs, such as long charging times, higher costs, short driving ranges, etc. Accordingly, this article explores the literature to understand how to ensure the reliable and safe operation of future electric road technologies and system deployments in municipalities. This study examines how ERSs power e-buses without relying solely on batteries. Additionally, this article analyzes the technological, legal, political, economic, and social factors that impact the electrification of road transport. Grounded in the literature, this study suggests that ERSs offer a cost-effective option to electrify heavy-duty transport, such as e-buses and e-trucks. The study provides insights for road authorities, municipalities, and policymakers to improve ERS deployment strategies, ensuring sustainable transportation while decarbonizing road transport. Full article

To address inaccurate longitudinal speed acquisition, difficult road adhesion identification, and insufficient reliability of state inputs for traction control in dual-motor electric vehicles under low-adhesion, adhesion-transition, and drive-slip conditions, this paper proposes a state estimation method oriented to traction control. Four-wheel speeds, inertial measurement unit (IMU) signals, and vehicle dynamics are fused to establish a layered longitudinal speed estimation structure, including slip-confidence evaluation, inertial correction, kinematic and dynamic fusion, and multi-mode weight decision. Standard road adhesion curves, fuzzy inference, and recursive correction are further combined to estimate the peak adhesion coefficient and the optimal slip ratio online. CarSim/Simulink co-simulation results show that the root mean square errors of the proposed speed estimation method are 0.1226, 0.1728, 0.1070, and 0.0322 m/s under comprehensive driving, acceleration slip, emergency braking, and high-speed steering conditions, respectively. Under an adhesion-transition condition, the peak adhesion coefficient and optimal slip ratio can be updated rapidly with road changes. Application results suggest that the estimated states can provide useful inputs for front–rear axle traction coordination under the investigated low-adhesion conditions. Full article

For single-motor Formula Student Electric China (FSEC) race cars, current powertrain design methodologies commonly suffer from the disconnection among parameter matching, dynamic simulation, and structural optimization: gear ratio selection is mostly based on static theoretical calculations, lightweight design does not incorporate full-vehicle dynamic load spectra constraints, and the simulation toolchain (CarSim 2024, OptimumLap version 5, ANSYS 2022) lacks a standardized data closed-loop, leading to prolonged iteration cycles and unquantifiable reliability. To address these issues, this paper takes the Nanning University electric formula race car E66 as the research object and proposes a three-phase integrated design framework of “requirement-driven, multi-simulation co-validation, and lightweight iteration.” The study includes three core contributions: (1) establishing a powertrain parameter matching method based on power boundary calculations and multi-dimensional selection criteria, achieving the integrated selection of the Emrax 228 motor (power density 9.2 kW/kg, Emrax d.o.o., Kamnik, Slovenia) and the Unitek-D3 controller through comparative analysis with the JJE motor (5.7 kW/kg, Jing-Jin Electric Technologies Co., Ltd., Beijing, China); (2) constructing a co-simulation mechanism combining OptimumLap version 5 and CarSim 2024, completing the closed-loop optimization of the gear ratio from the range of 1.6–4.3 to the optimal value of 3.9 under the Hefei NIO track operating conditions, with a 75 m acceleration simulation result of 4.4 s and an endurance lap time of 86 s; (3) introducing ANSYS 2022 topology optimization technology to perform two-iteration lightweight design on the 7075 aluminum alloy main sprocket, achieving 35% mass reduction and 40% volume reduction while maintaining the maximum principal stress at 73.16 MPa (below yield strength). The expected outcome is a replicable development paradigm for single-motor powertrain systems, transforming drivetrain matching from experience-driven to simulation-driven, providing reliable data boundaries for physical vehicle commissioning, and effectively reducing trial-and-error costs. Full article

In this paper, the concept of symmetry is applied to design active trajectory tracking control of autonomous distributed drive electric vehicles considering energy efficiency—that is, the construction and solution of active trajectory tracking controllers are symmetrical. This paper proposes a hierarchical control strategy consisting of upper-level control and lower-level control to improve trajectory tracking accuracy of DDEVs considering energy-efficiency characteristics. In the upper-layer control, a sliding mode active disturbance rejection (ADRC) controller is developed to control the front wheel steering angle and active yaw moment to achieve tracking of the desired trajectory, in which an extended state observer (ESO) is synthesized to estimate and compensate for internal model uncertainties and external environmental disturbances. In the lower-layer control, a multi-objective optimization algorithm based on Karush–Kuhn–Tucker (KKT) conditions is designed to realize the torque distribution control for improving energy efficiency and vehicle stability of the distributed drive electric vehicle. Finally, a joint simulation platform based on Matlab/Simulink-CarSim (version 2019) is established for simulation verification. The performances of ADRC, linear quadratic regulator controller (LQR), and model predictive controller (MPC) are compared in snake-like and double-lane-change maneuvers. Simulation results show that the proposed controller can effectively reduce motor energy consumption while maintaining trajectory tracking accuracy and handling stability. This work provides a certain engineering design solution for motion control of intelligent electric vehicles. Full article

Vehicle energy-regenerative suspension can convert part of the vibration energy induced by road excitation into electrical energy. However, there are coupled performance conflicts among energy recovery, ride comfort, and suspension safety, and a fixed-weight LQR controller finds it difficult to maintain a reasonable performance compromise under different road conditions. To address this problem, this paper proposes an AWG-NN-LQR control method based on an Adaptive Weight Generation neural network. First, a quarter-car energy-regenerative suspension model, an electromagnetic actuator model, and a random road model are established, and the vertical vehicle responses and energy-regeneration characteristics under different road classes are analyzed. Second, vehicle speed, road roughness coefficient, and statistical features of vehicle responses are used as inputs. LQR weight labels are generated through offline closed-loop simulation and candidate-weight search, and the AWG-NN is trained to learn the nonlinear mapping relationship between road conditions and weight parameters. Finally, closed-loop comparative validation is conducted for the passive suspension, fixed-weight LQR, and AWG-NN-LQR under a typical class-C road condition. The results show that, compared with the fixed-weight LQR, AWG-NN-LQR reduces the RMS of body acceleration from $1.7041{\mathrm{m}/\mathrm{s}}^2$ to $1.6527{\mathrm{m}/\mathrm{s}}^2$, and reduces the RMS of suspension deflection from $0.00863\mathrm{m}$ to $0.00844\mathrm{m}$, while achieving an average regenerated power of $9.41\mathrm{W}$. The proposed method can improve the objective-bias problem of the fixed-weight LQR under a typical operating condition while maintaining a certain energy-regeneration capability, providing a feasible approach for multi-objective adaptive control of energy-regenerative suspension. Full article