International Journal of Vehicle Systems Modelling and Testing (9 papers in press)

Regular Issues

• Game theory-based lane-changing decisions in adverse weather conditions  
  by Jian Ma, Zheng Qian, Liyan Zhang, Xiaofei Hu, Keyi Cao, Qianlong Fu 
  Abstract: Adverse weather conditions significantly impact driving safety and greatly increase road accidents. Lane-changing is essential for safe and efficient vehicle operation and the factors influencing lane-changing behaviors are very complicated under adverse weather. This study presents an improved lane-changing safety distance model by introducing the fuzzy evaluation quantification value of severe weather for the decision-making of lane-changing. In addition, the Gazis-Herman-Rothery model, an anchoring effect model and the full velocity difference model are refined. Based on a non-cooperative dynamic game (NCDG) model, a joint simulation environment is developed using Python. Simulation of urban mobility is conducted to simulate experiments. The simulation results indicate that, using the improved lane-changing model and the NCDG model, vehicles in adverse weather conditions are able to select the optimal driving strategy in interactive conflict scenarios, effectively alleviating conflicts and enhancing driving efficiency and safety.
  Keywords: severe weather; NCDG; non-cooperative dynamic game; lane-changing decision; SUMO.
  DOI: 10.1504/IJVSMT.2025.10071855
   
  
• Construction of traffic accident knowledge graph based on the correlation analysis of risk factors  
  by Liyan Zhang, Keyi Cao, Jian Ma, Yuan Wen, Zheng Qian, Yuchen Zhang 
  Abstract: With an increase of vehicles, traffic accidents have also risen. This paper presents a novel approach to creating a traffic knowledge graph using a keyword extraction algorithm to analyze accident data from a specific city, focusing on identifying key terms related to the causes of accidents. The data are analyzed from four aspects: human factors, vehicles, road conditions and environmental factors, to construct the knowledge graph. The findings indicate that the improved TextRank algorithm, which incorporates word vectors and a multi-feature weighting mechanism, outperforms traditional TextRank and inverse document frequency methods in keyword extraction. The present TextRank algorithm effectively combines word-specific attributes and structural features, delivering better extraction performance.
  Keywords: traffic accidents; knowledge graph; TextRank algorithm; keyword extraction.
  DOI: 10.1504/IJVSMT.2025.10072046
   
  
• Evaluating two control cases of pneumatic suspensions on the working performance of heavy vehicles  
  by Li Zhang, Hui Zan 
  Abstract: To evaluate the performance of control pneumatic suspensions using the air valve control and damping control on the working performance of heavy vehicles (HV), an HV model is established to simulate dynamic equations under excitations of ISO-B to ISO-E. Optimal controls of the fuzzy logic optimized by the genetic algorithm are applied to control pneumatic suspensions. HV’s working performance is evaluated through two indexes of the comfortable level and road friendliness. Results show that both control cases of the air valve and damping value greatly ameliorate HV’s working performance in comparison with passive pneumatic suspensions. However, the road friendliness with air valve control is higher than the road friendliness with damping control. This means that HV’s working performance using air valve control is better than damping control. Therefore, the air valve control of pneumatic suspensions should be applied to ameliorate HV’s working performance further.
  Keywords: heavy vehicle model; pneumatic suspensions; optimal control methods; comfortable level; coefficient of dynamics load.
  DOI: 10.1504/IJVSMT.2025.10072320
   
  
• Wheel polygonal wear of metro vehicles caused by wheel-rail P2 force resonance  
  by Shi Yixuan, Qingzhou Mao, Huanyun Dai, Cuijun Dong 
  Abstract: Wheel polygonal wear significantly affects vibration performance of rail vehicles. In this study, dynamic track test is carried out and dynamical modelling for a vehicle-track vertical coupling system is developed. Results show that the wheel polygonal wear of order 7-9 occurs in the measured vehicle, resulting in the forced vibration with frequencies of 50-70 Hz, which is close to the resonant frequency of wheel-rail P2 force under vehicle-track coupling conditions. The polygonal vibration of the wheel on floating slab track is caused by the wheel-rail P2 force resonance, the frequency is consistent with the wheel polygonal characteristic frequency around 60 Hz, and it is the periodic irregular rail welded joints in a line that trigger the P2 force resonance mode. This article explores the mechanism of metro wheel polygonal wear formation in terms of vehicle-track coupling effects.
  Keywords: metro vehicle; wheel polygonal wear; vehicle-track coupling; wheel-rail P2 force; dynamic test.
  DOI: 10.1504/IJVSMT.2025.10072435
   
  
• A new control method of an automobile's pneumatic suspension systems based on machine learning algorithm  
  by Jiao Renqiang, Vanliem Nguyen, Li Zhang 
  Abstract: Based on control rules of fuzzy-logic-control (FLC) optimised by genetic-algorithm, a new control method of machine-learning-algorithm (MLA) developed by an adaptive-neuro-fuzzy-inference-system (ANFIS) is used to learn these optimal control rules. MLA is then applied to control automobiles pneumatic-suspension-systems (PSS) to improve automobiles comfort. MLAs performance is then evaluated under automobiles different conditions on rigid roads and deformed terrains. Some outstanding results include: 1) the automobiles comfort moving on the deformed terrain is worse than on the rigid road. Thus, the automobile should move at low speed on the deformed terrain to ensure the comfort; 2) with MLA used, its control performance is relatively stable under all different operating conditions of the automobile; and 3) with control data maps of MLA learned from FLCs optimal rules, MLA improves the automobiles comfort better than FLC under various simulation conditions. Therefore, this new control method of MLA should be considered for application in automobile suspension systems.
  Keywords: control pneumatic suspensions; automobile's dynamic model; FLC; MLA; automobile's comfort.
  DOI: 10.1504/IJVSMT.2025.10073302
   
  
• Lightweight design of automotive parts based on the FPTO method  
  by Dengfeng Huang, Xuwei Hu, Xiaolei Yan 
  Abstract: Topology optimisation is a method to maximise or minimise an objective function by optimising material distribution under design constraints. In the theoretical research of topology optimisation, most algorithms are developed on regular geometric models in software such as MATLAB. However, applying these findings directly to complex structures with irregular geometries in engineering is challenging. Optimisation of such structures relies on commercial software using density-based methods, hindering open algorithm research. The floating projection topology optimisation (FPTO) is a stable, efficient method producing good results. This study introduces the FPTO principles and investigates its integration on the MATLAB-ABAQUS platform, including conducting analysis in ABAQUS, performing optimisation solution and result visualisation in MATLAB, and facilitating data exchange. The boundaries of the topology structure are smoothed to better meet actual engineering requirements. This research explores the application of FPTO to automotive components, achieving lightweight designs for wheel hubs and control arms, offering an effective engineering solution.
  Keywords: topology optimisation; floating projection; MATLAB-ABAQUS platform; complex structures.
  DOI: 10.1504/IJVSMT.2025.10073303
   
  
• Effects of Endwall Slot Jet Angles on the Performance of High-Load Compressor Cascade  
  by Junfu Yuan, Chen Li 
  Abstract: The three-dimensional corner separation represents a major obstacle to enhance peformance of a high-load compressor cascade. To tackle this challenge, we conducted a CFX numerical simulation to investigate impact of the endwall slit jet parameters on performance of the compressor cascade. Numerical analysis revealed that the endwall slot jets substantially suppressed the corner separation and reduced flow loss. At a jet flow rate comprising a mere 0.435% of the mainstream flow rate, a 30
  Keywords: High load diffuser cascades; End wall slit jet; Flow control; Angular separation.
  
  
• Data-driven material constitutive modelling: a framework for method selection and performance evaluation  
  by Hengli Yu, Yantao Wang, Yingjing Wang, Tong Pang, Tangying Liu 
  Abstract: Constitutive models are crucial in engineering design and simulations. Traditional models require tedious parameter calibration and have limited generalization capabilities, while data-driven models offer adaptive learning advantages. However, selecting optimal models remains challenging. This study evaluates eight data-driven methods in material constitutive modeling across sparse and dense parameter spaces. By analyzing fitting accuracy, interpolation capability, and extrapolation performance, we found that deep neural networks provide the most stable generalization in sparse parameter spaces, while kriging achieves near-perfect performance in dense parameter spaces. Based on these findings, we propose a systematic model selection framework that considers data sampling density and prediction task types, providing a theoretical foundation for model selection across various material constitutive models.
  Keywords: constitutive modeling; data-driven methods; deep neural network; kriging.
  DOI: 10.1504/IJVSMT.2025.10074803
   
  
• The integration of neural networks in visual effects design for new energy vehicles  
  by Lijuan Sha, Jingyu Li, Chunxu Zhang, Xiangjuan Liu, Shiqing Lu, Bincheng Zuo, Shuxin Chen 
  Abstract: As the intelligent automotive industry thrives, artificial intelligence has deeply integrated into automotive design, driving the advancement of new energy vehicle (NEV) design. The focus lies in catering to users design preferences to provide utmost interior comfort. This study utilises representative automotive exterior samples to train a backpropagation neural network model and to establish correlations between design element encodings and sensory evaluation metrics. The effectiveness is tested via samples. Additionally, users sensory imagery words are collected for factor analysis. Subsequently, the exterior samples are refined; sensory word groups are identified; and the model is retrained through reassembly. This integration of AI and user sensory imagery analysis provides systematic methodologies for designers and aids in project design. Ultimately, based on factors such as colour, material and surface treatment, an ideal exterior sample for NEVs is predicted, offering a fresh perspective and approach to NEV design.
  Keywords: Kansei engineering; BP neural network; machine learning; style transfer; NEV; new energy vehicle.
  DOI: 10.1504/IJVSMT.2025.10074877