International Journal of Vehicle Design (13 papers in press)

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• This is a test paper, pleaseignore it
  by ReviewerV ReviewerC 
  Abstract: This is a test submission. Please ignore it
  Keywords: test test test test test test test test test test test test test test test test.
  
  
• Research on MPC path tracking control based on adaptive adjustment of near and far preview distance  
  by Shaosong Li, Detao Li, Feizhang Feng, Kai Zhang, Xiaohui Lu, Gaojian Cui 
  Abstract: Path tracking capability is a pivotal element of intelligent vehicle motion control technology. This paper introduces an innovative path tracking control method, integrating near and far-point previews, to enhance path tracking precision and vehicle stability. Employing fuzzy rule design, an adaptive near-point preview model is developed, dynamically adjusting the near-point preview distance by considering vehicle speed, path deviation, and road curvature information. Building upon this foundation, a path tracking controller is crafted using model predictive control (MPC) to enhance adaptability and tracking performance to the path. Furthermore, the far-point preview distance is determined based on vehicle speed and dynamic characteristics, thereby establishing the desired longitudinal speed of the vehicle. Subsequently, closed-loop control of longitudinal velocity is implemented to bolster safety and traffic efficiency during path tracking. Ultimately, a collaborative simulation platform, merging Carsim and Matlab/Simulink, is established to authenticate the effectiveness of the proposed control algorithm.
  Keywords: path tracking; adaptive preview; model predictive control; longitudinal speed control.
  DOI: 10.1504/IJVD.2025.10077355
   
  
• Battery anti-ageing improvement for hybrid energy storage system in plug-In hybrid electric vehicle using historical information passing network  
  by S. Mythili, S.S. Sivaraju, S. Chitra Selvi, R. Karthick 
  Abstract: The automotive industry increasingly focuses on plug-in hybrid electric vehicles (PHEVs) to reduce energy consumption and carbon emissions. However, high instantaneous power demands during driving cause frequent battery charging and discharging, which accelerates battery aging. To address this, this paper proposes a novel approach to extend battery life for hybrid energy storage systems (HESS) in PHEVs. The system combines the Historical Information Passing Network (HIPN) and Wombat Optimization Algorithm (WOA), referred to as WOA-HIPN. The WOA optimizes the power and size of the HESS, while HIPN predicts system performance. The goal is to prolong battery life and reduce operational costs. The proposed technique is tested in MATLAB and compared with existing methods, including Genetic Algorithm (GA), Back Propagation Neural Network (BPNN), and Particle Swarm Optimization (PSO). Results show that WOA-HIPN outperforms these techniques, reducing the operational cost to $3877.5, compared to $4966.6, $5517.5, and $6151.6 for the existing approaches.
  Keywords: supercapacitor; battery life improvement; DC/DC converter; energy management; HESS; hybrid energy storage system; electric vehicle; PHEVs; plug-in hybrid electric vehicles.
  DOI: 10.1504/IJVD.2025.10078606
   
  
• Intelligent vehicle path planning algorithm based on the fusion of improved RRT and dynamic window method  
  by Maofei Zhu, Fangya Hu, Nake Li, Wei Sha, Ao Zhao 
  Abstract: A smart vehicle path planning algorithm based on the fusion of improved rapidly-exploring random tree (RRT) and dynamic window method is proposed to address the problems of traditional RRT algorithms path planning not meeting vehicle motion characteristics, low efficiency, and the tendency of traditional dynamic window approach (DWA) algorithm to fall into local optima. Firstly, in RRT node expansion, a multi-sampling strategy and A* heuristic are introduced, followed by vehicle kinematics constraints to obtain sampling points. Secondly, the obtained initial path removes redundant points and extracts the critical points on the path, as well as constructing the evaluation function with the global path critical point information, which serves as the foundation for the DWA algorithm to plan the local path. Finally, the simulations in Matlab and real experiments on ROS platform demonstrate that the proposed algorithm rapidly generates global paths and effectively avoids unknown obstacles.
  Keywords: path planning; fast extended random tree; dynamic window method; fusion; obstacle avoidance.
  DOI: 10.1504/IJVD.2026.10079039
   
  
• An ergonomic approach to carsharing design for future vehicles: an evaluation of interior and exterior design proposals in medium-term and long-term perspectives  
  by Kevin Guelle, Barré Jessy, Natacha Métayer 
  Abstract: Carsharing offers a means to reduce vehicle fleets and is increasingly integrated into mobility services; however, it remains underutilised. This study aims to evaluate design concepts intended to enhance user intention. A three-phase methodology was employed. First, creativity sessions produced 8 Interior Design Proposals (IDPs) and 8 Exterior Design Proposals (EDPs). Second, after evaluation by experts, one IDP (good smell diffuser) and one EDP (retrofit) were selected. Third, an online survey (N = 236) assessed the influence of these design proposals on declared willingness to use carsharing. Findings indicate that the EDP positively affects the intention to use carsharing services in both the medium-term and long-term. In contrast, the IDP elicited less favourable responses but underscored the significance of sensory and comfort considerations in carsharing vehicles. The study underscores the need to address social and ergonomic aspects of carsharing vehicles to promote broader adoption.
  Keywords: carsharing; vehicle designs; prospective ergonomics; interior designs; exterior designs; sustainable mobility.
  DOI: 10.1504/IJVD.2026.10079109
   
  
• A lightweight model for detecting traffic signs  
  by Zhang Rongyun, Zheng Kunming, Shi Peicheng, Xu Yuxiang, Zhou Bingzhou 
  Abstract: Accurate detection of traffic signs is a prerequisite for ensuring the safety of intelligent-assisted driving vehicles. This article proposes a structure based on Ghost convolution and CBAM attention mechanism cascaded with YOLOv5's backbone feature extraction network to optimize it, thereby reducing model complexity and increasing the detection speed of traffic signs. The WIoU is adopted as the bounding box loss function, and a CARAFE upsampling module is introduced in the feature fusion layer to better recover local details of the detection targets. Simulation results show that the improved YOLOv5 model achieves a 1.8% increase in mAP, reaching 91.9%, while reducing the model's weight by 65.1% and its parameter count by 58.8%. Additionally, the detection speed increases by 8.6 frames per second (f/s).
  Keywords: Traffic sign recognition; Lightweight; YOLOv5; GhostNet.
  
  
• Adaptive inverter nonlinear compensation and full parameter online identification of permanent magnet synchronous motor based on DEKF algorithm  
  by Yong Li, Jiexin An, Han Hu, Xing Xu 
  Abstract: Inverter nonlinearities can significantly deteriorate the accuracy of parameter identification for permanent magnet synchronous motors (PMSM). To address this issue, this paper proposes a full parameter identification method of surface permanent magnet synchronous motor (SPMSM) considering inverter nonlinearity based on a dual extended Kalman observer. First, a mathematical model of the motor incorporating inverter-induced disturbance voltages is established. Then, a dual extended Kalman observer is designed to achieve high-accuracy multi-parameter identification. Meanwhile, the identified nonlinear disturbance voltages are fed into the control system for dead-time compensation, thereby suppressing the adverse effects of inverter nonlinearities on identification accuracy. The results of the simulation and motor test bench demonstrate that the proposed method can effectively compensate for inverter nonlinear disturbances, with multi-parameter identification errors below 5%, and exhibits excellent disturbance rejection performance under various operating conditions.
  Keywords: PMSM; permanent magnet synchronous motor; Extended Kalman filter algorithm; online parameter identification; dead-time compensation; inverter nonlinearity.
  DOI: 10.1504/IJVD.2025.10079232
   
  
• Advancements in multi-source converters for electric vehicles: a comprehensive review and experimental analysis  
  by Siddhant Gudhe, Sanjeev Singh 
  Abstract: The integration of multiple energy storage systems in electric vehicles (EVs) presents a promising approach to optimising operational efficiency. This research addresses the critical challenge of enhancing EV drive system efficiency through the novel application of a multi-source converter (MSC) and a comprehensive battery management system (BMS). By linking two distinct DC energy sources directly to the traction motor and eliminating the need for a DC/DC boost converter, this approach significantly improves overall efficiency and aligns with sustainable transportation goals. The study analyses various MSC topologies for EV applications, emphasising their environmental benefits, bidirectional charging capabilities, and the dynamic control facilitated by the BMS. Field-oriented control with space vector modulation ensures harmonised operation of the traction motor. Simulation results in MATLAB/Simulink validate the bidirectional capabilities and efficiency improvements, while a scaled-down experimental setup demonstrates the dynamic behaviour of the traction motor powered by an MSC, underscoring its practical applicability.
  Keywords: MSC; multi-source converters; bidirectional power flow; electric vehicles; neutral point clamped converter; single-phase on-board charger; SVPWM; space vector pulse width modulation.
  DOI: 10.1504/IJVD.2025.10076338
   
  
• Stator and rotor temperature prediction for water-cooled electric vehicle motors based on BP neural network  
  by Yong Li, Keke Sheng, Xing Xu, Heping Ling 
  Abstract: In this paper, we propose a stator and rotor temperature prediction for a water-cooled electric vehicle motor based on a back propagation (BP) neural network. Firstly, a computational model of the permanent magnet synchronous motor (PMSM) is established based on the main parameters of the PMSM, and the PMSM losses are calculated and analysed. Then, a three-dimensional field computational model of the PMSM is established, the temperature field simulation boundary conditions are solved, and the temperature field analysis is carried out. Moreover, the computational fluid dynamics (CFD) verifies the accuracy of the one-dimensional thermal network model. Finally, the technique uses the one-dimensional thermal network model to construct a training set and builds the PMSM stator and rotor temperature prediction model based on BP neural network training. Compared with the CFD, the computation time of the model is greatly reduced while maintaining sufficient accuracy.
  Keywords: PMSM; permanent magnet synchronous motor; BP neural network; CFD; computational fluid dynamics; one-dimensional thermal network; temperature prediction.
  DOI: 10.1504/IJVD.2025.10076710
   
  
• Research on torque filtering control strategies and system optimisation in electric vehicles  
  by Kunjun Wang, Ye Xiao, Liang Wang 
  Abstract: With the rapid evolution of the electric vehicle (EV) industry, torque control has emerged as a critical aspect of EV technology, presenting unique challenges in achieving optimal vehicle performance and efficiency. This paper delves into the challenges of torque control in EVs, particularly during the critical transition phases between driving and braking. We introduce a novel torque filtering control method, meticulously designed to optimise torque rise and fall rates and implement effective torque zero-crossing management. This approach swiftly accommodates the driver's torque requests, while markedly diminishing the impact and noise caused by the reducer gear meshing when the driving and braking conditions switch to each other, thus elevating both vehicle comfort and safety. The method's efficacy was rigorously tested and validated under diverse conditions using a 10-metre pure electric bus, demonstrating notable improvements in vehicle stability and passenger comfort, especially in complex conditions. This research contributes a robust solution to torque control challenges in EVs, marking a significant stride in the technological evolution of electric mobility.
  Keywords: electric vehicles; torque control; torque filtering; control strategy; driving comfort.
  DOI: 10.1504/IJVD.2025.10077228
   
  
• Adaptive dynamic surface control for car-following tasks based on probabilistic prediction of preceding vehicle's future motion states  
  by Jinghua Guo 
  Abstract: This paper proposes a novel adaptive dynamic surface control scheme for car-following tasks based on the probabilistic prediction of the preceding vehicle's future states. Firstly, the vehicle driving process in a short time is regarded as a discrete Markov chain, and the preceding vehicle's potential speed and acceleration for the car-following control system are predicted for a preset prediction time horizon via the Markov Chain Monte Carlo (MCMC) technique. Then, an adaptive dynamic surface control system for car-following tasks is constructed to handle the characteristics of parametric uncertainties, external disturbances and nonlinearities of vehicles, in which the certain term is estimated by the fuzzy logic technique in real-time, and the stability of the proposed adaptive car-following control system is proven by the Lyapunov theory. Finally, the performance of the proposed adaptive dynamic surface control scheme for car-following tasks is evaluated by experimental tests, and the results show that the proposed control scheme can achieve preferable performance and high control precision.
  Keywords: intelligent vehicles; longitudinal dynamics; adaptive car following control; probabilistic prediction; uncertainties.
  DOI: 10.1504/IJVD.2026.10077354
   
  
• A new multi-objective MPC-based adaptive cruise control with dynamic variation safe distance considering road conditions  
  by Xiaohui Lu, Xusheng Dong, Yangqun Fan, Shaosong Li, Niaona Zhang, Liyuan Tian 
  Abstract: The tyre-road friction coefficient (TRFC) is pivotal for ensuring vehicle-following safety and optimising road utilisation in adaptive cruise control (ACC) applications. Therefore, based on real-time TRFC estimation, this paper introduces a new dynamic variation safe distance (DVSD) strategy and an innovative multiple-objective ACC algorithm. The hierarchical structure is utilised in which the lower layer controller compensates for nonlinear vehicle dynamics, ensuring precise tracking of the desired acceleration. In the upper layer controller, employing the model predictive control (MPC) algorithm and considering the impact of road conditions on vehicle dynamic control, we introduce TRFC as a variable constraint to enhance its suitability for real-world scenarios. Simulation results demonstrate that the proposed multi-objective MPC-based ACC controller with DVSD achieves superior control effects and reduces tracking errors compared to the conventional MPC controller. Furthermore, compared with the traditional (CTH) spacing strategy, it significantly enhances vehicle-following safety and increases road utilisation rates.
  Keywords: ACC; adaptive cruise control; spacing strategy; MPC; model predictive control.
  DOI: 10.1504/IJVD.2025.10078986
   
  
• Simulation-based evaluation of speed-adaptive steering ratios in four-wheel steer-by-wire vehicles  
  by Samuel Sonnino, Stefano Melzi, Giacomo Mirra, Pietro Caresia, Alessandro Manzoni, Gianluca Vaini 
  Abstract: Vehicle handling requires a careful balance between agility at low speeds and stability at high speeds, a trade-off that traditional fixed-link steering systems struggle to address. Steer-by-wire (SBW) technology removes the mechanical connection between the steering wheel and wheels, enabling real-time, independent adaptation of the steering ratio at both front and rear axles. This work presents a preliminary simulation-based evaluation of a speed-adaptive variable steering ratio strategy applied to both axles, aimed at improving handling under diverse conditions. Validation was performed through offline ISO standardised open-loop manoeuvres and driver-in-the-loop (DiL) simulations, providing early subjective insights. While results indicate potential improvements in manoeuvrability and stability, the current validation underscores the need for further research and broader experimental evaluation to confirm these findings. This study lays the groundwork for future investigations into optimising speed-adaptive steering in four-wheel SBW systems.
  Keywords: SBW; steer-by-wire; four wheel steering; variable steering ratios; vehicle stability; driving simulator.
  DOI: 10.1504/IJVD.2025.10078073