International Journal of Vehicle Systems Modelling and Testing (14 papers in press)

Regular Issues

• TriXAI twin: a threefold explainability approach for battery health monitoring in electric vehicles  
  by P.G. Parvati, Mehbooba Shareef 
  Abstract: When electric vehicles are redefining modern transportation, the ability to monitor and predict battery health in real-time becomes critically important for ensuring safety, reliability and energy efficiency.This paper introduces deep learning framework based on a Long Short-Term Memory(LSTM) for accurate prediction of State of Charge(SoC) and State of Health(SoH) of lithium-ion batteries using NASA battery dataset.However the opacity of deep learning(DL) models often limits their adoption in safety critical application like battery management.To overcome the interpretability challenges of DL models, a hybrid explainability framework combining SHAP(SHapley Additive exPlanations), LIME(Local Interpretable Model-agnostic Explanations), and Layer-Wise Relevance Propagation(LRP) is introduced, which enables both global and local feature attribution.This unified framework not only delivers high predictive accuracy but also unveils temporal and contextual significance of features driving the model's decisions, making it suitable for reliable decision making in EV enabled battery systems.
  Keywords: BMS; battery management system; LSTM; long short-term memory; XAI; explainable artificial intelligence; LIME; local interpretable model-agnostic explanations; SHAP; Shapley additive explanations; LRP; layer-wise relevance propagation; DT; SoC; state of charge; SoH; state of health.
  DOI: 10.1504/IJVSMT.2025.10075067
   
  
• Constructing question and answer system of urban rail transit emergencies  
  by Jiashuai Li, Bosong Fan, Xuejin Wan, Dong Liu, Dan Zhao 
  Abstract: Urban rail transit systems face challenges such as high data costs, complex knowledge construction, and significant differences across domain-specific datasets in constructing question and answer (Q&A) systems. This paper presents the urban rail transit operation emergency (URTOE)-large language model (LLM). The Q&A system construction method is based on LLM and domain-specific knowledge. URTOE-LLM introduces a domain knowledge enhancement mechanism and optimizes model parameters to meet the specific needs of urban rail transit. The model leverages an image-text multimodal expert and uses a phased pre-training approach to efficiently learn and retrieve of information of the operational emergency. The model's performance is evaluated using an urban rail transit operational emergency log dataset by comparing it with traditional models. The results demonstrate that URTOE-LLM outperforms other models, achieving precision of 0.87 in key phrase and relation recognition tasks.
  Keywords: urban rail transit; operational emergencies; large language modelling; domain question; answer system.
  DOI: 10.1504/IJVSMT.2025.10075692
   
  
• Data augmentation to enhance the accuracy of deep neural networks in subjective vehicle dynamics evaluation  
  by Cádmo Rodrigues, Jánes Landre Jr 
  Abstract: The accuracy of deep neural networks highly depends on the quality and representativeness of the data input. In the context of vehicle adaptation based on driver preferences, inconsistencies in data collection and noise in realworld driving conditions often hinder virtual model performance. In this context, this study proposes a novel data processing algorithm designed to enhance training data through augmentation and normalisation techniques. To do so, this algorithm relies on statistical transformations applied to the original telemetry signals. After perturbing the mean and variance of selected parameters within the observed experimental limits to generate synthetic samples, the new data retained the same statistical nature as the original dataset, and the plausibility of the augmented dataset was further validated using the KolmogorovSmirnov (KS) test. By refining the dataset and implementing an optimised training loop, a significant reduction in prediction errors and improved model generalisation is achieved. Since the developed system demonstrated a gain in accuracy from less than 25% to about 90% during vehicle evaluation in a driver-in-the-loop dynamic simulator, this work introduces two main innovations: The first involves attenuating the subjectivity inherent in individual driving style preferences, a critical challenge currently faced by the automotive industry; the second refers to the methodology for data collection and processing, validated through statistical tests, ensuring the reliability of the generated results. The experimental results demonstrated that the proposed approach enhances neural network accuracy, leading to more reliable adaptations of vehicle behaviour according to individual driver preferences. These findings highlight the importance of data preprocessing in machine learning applications and open new possibilities for personalised driving experiences.
  Keywords: driver-in-the-loop simulation; vehicle dynamics; subjectiveness in simulation; DiM 150; driver preferences; vehicle data augmentation; machine learning; deep learning.
  DOI: 10.1504/IJVSMT.2025.10076148
   
  
• Multi-closed-loop compound control strategy for braking gap adjustment in electromechanical brake systems  
  by Qiping Chen, Yuanhao Cai, Wuhao Xu, Qiang Shu, Wangliang Fu 
  Abstract: To address the issues of the increased volume and cost associated with traditional electromechanical brake systems (EMB) due to additional mechanical or sensing mechanisms for gap control, this paper proposes a braking gap adjustment strategy based on a multi-closed-loop compound control framework. First, a multi closed-loop EMB motor control system is firstly developed by employing a genetic algorithm-optimized proportional integral derivative (GA-PID) controller to regulate the clamping force. Subsequently, a braking gap adjustment method is then introduced to identify critical points for different braking stages and apply targeted control objectives accordingly. The EMB system and control strategy are modeled and validated through co-simulation using CarSim/Simulink. Simulation results demonstrate that the proposed GA-PID controller ensures rapid and precise clamping force regulation. Furthermore, the gap adjustment strategy effectively reduces response time, maintains optimal brake clearance, and mitigates adverse conditions caused by pad wear, thereby enhancing EMB safety and reliability.
  Keywords: electro-mechanical braking system; critical point; multi-closed loop; genetic PID; gap adjustment.
  DOI: 10.1504/IJVSMT.2025.10076149
   
  
• Dynamic modelling and state estimation of hub motor-driven amphibious vehicles for water-to-land transition  
  by Bin Huang, Wenbin Yu, Lianbing Suo, Zineng Yuan 
  Abstract: This study investigates the modelling and state estimation of a hub-motor-driven amphibious vehicle under water-to-land transition conditions. The focus is on modelling during land-to-water transitions and the design of an effective estimation framework. Accurate estimation must consider complex coupled effects such as buoyancy, fluid resistance, low-adhesion surfaces, and dynamic slopes. To address this, a Fading Memory Unscented Kalman Filter (FM-UKF) is proposed, integrating wheel speed and acceleration for vehicle velocity estimation. A longitudinal slope estimation method is also developed by fusing acceleration sensor data with a Forgetting Factor Recursive Least Squares (FF-RLS) algorithm. A multibody dynamics model is established, enabling real-time estimation of longitudinal speed, terrain slope, and adhesion coefficient through the proposed fusion framework. Simulation results demonstrate the method's effectiveness under transition conditions. Additionally, a real-vehicle experiment on a multi-gradient ramp confirms the accuracy and practicality of the proposed approach in real amphibious driving scenarios.
  Keywords: amphibious vehicle; state estimation; water-to-land composite conditions; multibody dynamics model.
  DOI: 10.1504/IJVSMT.2025.10076258
   
  
• Reinforcement learning-based dynamic pricing research on ride-hailing platforms  
  by Yue Bai, Wei Zhang 
  Abstract: Due to the significant spatiotemporal dynamics and the complex supply-demand relationship in ride-hailing operations, this study proposes a multi-module dynamic pricing model for ride-hailing (MDPM-RH) platforms, which is capable of real-time sensing of regional supply-demand fluctuations and constructing differentiated spatiotemporal price coefficients to enable dynamic pricing. The model considers the vacant vehicle dispatching and driver-passenger matching problems, and corresponding sub models were constructed separately. The dynamic pricing problem is formulated as a Markov decision process (MDP), and solved using the soft actor-critic (SAC) algorithm to maximise long-term profits. The experiments based on real ride-hailing order data in Haikou City show that the dynamic pricing method proposed in this study can effectively improve the platform profit. Compared with static pricing, the dynamic pricing strategy achieved a weekly profit improvement of
  Keywords: dynamic pricing; ride-hailing; reinforcement learning; soft actor-critic.
  DOI: 10.1504/IJVSMT.2025.10076340
   
  
• Selecting new energy vehicle product concepts: integrating best-worst method and quality function deployment  
  by Yan Gou, Jian-peng Chang, Jun Zhang, Qun Bai, Xing-yi Zou, Mei Cao 
  Abstract: Intense competition in the rapidly growing new energy vehicle market demands optimal product concept selection. Current methods often determine the importance of technical characteristics based primarily on customer requirements, neglecting critical factors like competitor performance and technological feasibility, and thus typically lack mechanisms to translate technical performance into subjective customer satisfaction for ranking. This paper presents a novel model integrating the Best-Worst Method and Quality Function Deployment to comprehensively evaluate technical characteristics by considering customer requirements, competitor performance, technological feasibility, and company strengths. Customer satisfaction is quantified using prospect theory's value function, enabling ranking based on comprehensive satisfaction scores. A case study of a traditional automaker entering the new energy vehicle market demonstrates the model's effectiveness. Findings emphasize aligning product development with customer preferences and market dynamics to improve competitiveness.
  Keywords: New energy vehicles; product concept selection; Best-Worst Method; Quality Function Deployment; prospect theory.
  DOI: 10.1504/IJVSMT.2026.10076384
   
  
• MATSim research on urban traffic simulation and optimisation strategy in collaboration with SUMO  
  by Jian Ma, Qianlong Fu, Liyan Zhang, Hairong Gu, Yuchen Zhang 
  Abstract: This paper presents mesoscopic and microscopic traffic simulations to address urban congestion challenges in a context of rapid urbanization. A co-simulation platform was developed by combining the multi-agent transport simulation (MATSim) agent-based modeling with the simulation of urban mobility (SUMO). The methodology incorporates deep Q-networks for adaptive traffic signal control and genetic algorithms for an optimal route planning, and the co-simulation approach can reduce bus waiting times by 29.10% and increased intersection throughput by 8.14% through a memory palace-enhanced DQN model for adaptive signal control., The genetic algorithms optimized route planning for 50,000 simulated residents. The utility-discrete choice framework improved travel demand realism by translating utility scores into probabilistic selections.Multi-stage optimization balanced system efficiency with individual preferences, proving 25% faster convergence than baseline DQN. This computationally efficient platform bridges macroscopic policy analysis with microscopic behavioral modeling, offering robust decision support for urban traffic management.
  Keywords: traffic simulation; MATSim; SUMO; joint simulation; congestion relief.
  DOI: 10.1504/IJVSMT.2025.10076966
   
  
• Task allocation for multi-unmanned vehicles under dynamic stochastic interference  
  by Xin Zhang, Hao Ge, Xuting Duan, Haiying Xia, Jianshan Zhou 
  Abstract: Dynamic environmental changes introduce random interference that can compromise performance of multi-unmanned vehicle systems. To mitigate these challenges, this paper proposes a novel dynamic task allocation approach based on a distributed market auction mechanism. Our method incorporates a priority-based conflict resolution strategy to effectively manage tie situations during auctions, ensuring fair and efficient task distribution. Additionally, an innovative task insertion scheme integrates new tasks into existing sequences without reordering all pending tasks, thereby reducing computational overhead while maintaining system stability. Simulation experiments in a Gazebo environment demonstrate that our approach significantly improves adaptability under conditions of vehicle failure and temporary task additions. The proposed framework is validated through comprehensive simulations, confirming its ability to handle both planned and unexpected scenarios with minimal performance degradation. This work sets a new benchmark for dynamic task management in unmanned systems.
  Keywords: dynamic task allocation; multi-unmanned vehicles; auction algorithm; vehicle priority; distributed market mechanism; conflict resolution; task insertion; stochastic interference; Gazebo simulation; travel cost optimisation.
  DOI: 10.1504/IJVSMT.2026.10077219
   
  
• Study on the effects of raster spacing and raster angle on the mechanical properties of FDM-printed parts  
  by Na Qiu, Jiaxuan Wu, Xiaomin Wu, Yifei Ren 
  Abstract: 3D printing is widely used for manufacturing various automotive components. However, structures fabricated using fused deposition modelling (FDM) technology exhibit anisotropic properties, making it crucial to study the impact of process parameters on their mechanical performance. This study investigates the influence of raster spacing and angle on the mechanical properties of FDM-printed structures. The results revealed that a 0
  Keywords: FDM; fused deposition modelling; automotive energy absorber; mechanical properties; fracture modes.
  DOI: 10.1504/IJVSMT.2025.10077270
   
  
• A lightweight methodology for trajectory prediction of powered two-wheeler in pre-crash scenarios  
  by Yong Han, Changyi Liu, Yanting LI, Di Pan, Hui LIu, Xiaojiang Lv, Koji Mizuno 
  Abstract: To tackle real-time trajectory prediction for powered two-wheelers (PTWs) in pre-crash scenarios, this study proposes TPtiny, a lightweight framework integrating an anchorfree detector (VPDet), a groupedconvolution tracker (MobileSort), and a VAEGAN hybrid predictor (VGAN). Evaluated on dense traffic and real accident data, TPtiny achieves comparable accuracy to stateoftheart models while being significantly smaller and faster, providing 218.8281.3 ms additional decision time. The framework demonstrates high efficiency, generalisability, and suitability for embedded vehicle safety platforms.
  Keywords: intelligent vehicle safety; trajectory prediction; deep learning; lightweight neural network; powered two-wheelers.
  DOI: 10.1504/IJVSMT.2025.10077538
   
  
• A web-based visualisation system for the impact of hazard on transportation using Cesium digital Earth  
  by Yang Feng, Shikun Xie, Zhen Yang 
  Abstract: As global climate change continues to worsen, natural hazards are occurring more frequently, affecting transportation operations. To effectively manage traffic control and hazard rescue operations during hazards, it is essential to have accurate geographic information on the location and impact of these hazards during such events, and to predict the future traffic affected by the hazards. To improve this process, a system by integrating advanced web geographic information system (GIS) technology with dynamic traffic network model was developed to visualise the impact of hazards on road traffic. A town named Hutiaoxia of Yunnan province was selected as a case study to perform 6 h simulation. The simulation results showed that the roads close to the landslide site experienced a reduction in average speed and congestion. This paper will aid in making accurate traffic control decisions and coordinating rescue efforts.
  Keywords: hazard; GIS; geographic information system; Cesium digital Earth; transportation structures; SUMO; simulation of urban mobility.
  DOI: 10.1504/IJVSMT.2025.10077539
   
  
• Research on crash safety based on composite battery box of new energy vehicle  
  by Yajun Chen, Bowen Yao, Hanghua Zhang, Zehuan Sui, Xiaoxiao Song, Fusheng Wang 
  Abstract: With the rapid growth of the electric vehicle industry, the crashworthiness of power battery systems has become a critical safety concern. This study investigates a carbon fibre reinforced polymer (CFRP) battery enclosure for an electric vehicle platform. A high-fidelity finite element model was developed using the progressive damage model *MAT_261 in LS-DYNA and validated under two representative crash scenarios: bottom impact and lateral compression. The novelty lies in establishing an integrated simulation experiment correlation framework for multi-condition crash assessment of composite enclosures. Numerical predictions show strong agreement with experiments, with peak load deviations below 3% and energy absorption errors within 4%. Damage evolution, stress concentration regions, and dominant failure mechanisms were captured. The composite enclosure satisfies regulatory safety requirements while achieving over 40% weight reduction compared with conventional metallic designs. The proposed methodology provides a reliable strategy for designing lightweight, crash-resistant battery systems.
  Keywords: FEA; finite element analysis; CFRP; carbon fibre reinforced polymer; crashworthiness; CFRP battery box; LS-DYNA *MAT_261; progressive damage model.
  DOI: 10.1504/IJVSMT.2026.10077787
   
  
• Dynamic analysis and strength verification of track beams for suspended monorail vehicles under pulsating wind loads  
  by Yongzhi Jiang, Shixin Zhang, Cheng Ping, Renxiang Chen, Wang Qunsheng 
  Abstract: This paper proposes a track beam strength analysis method preserving complex vibration characteristics. First, a wind-vehicle-bridge coupled dynamics model is established, using Park's numerical integration to solve dynamic responses and extract wheel-rail contact forces. Second, transient dynamics theory is applied with dynamic wheel-rail forces as moving loads for strength analysis, while track beam vibration indicators are checked against industry standards. Additionally, Ncode software calculates and verifies fatigue life. Results show the track beam's maximum dynamic stress is below yield strength; all vibration indicators meet requirements; and fatigue analysis confirms a minimum lifespan of 5.434
  Keywords: suspended monorail track beam; wind-vehicle-bridge coupled model; strength verification; fatigue life assessment; strongly coupled nonlinear characteristics.
  DOI: 10.1504/IJVSMT.2026.10078375