International Journal of Vehicle Design (6 papers in press)

Regular Issues

• 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.
  
  
• Tyre design based on improved vehicle multidisciplinary performance using two-step approximate optimisation with representative design indices  
  by Fengling Gao, Deng-feng Wang, Zhixin Wu 
  Abstract: To improve overall vehicle performance when using CDTire/3D model-based tyre design, a two-step approximate optimisation method that combines several representative design indices is presented. The process optimises both of the CDTire/3D model vector and scalar parameters. A vehicle road noise design index as the optimisation objective is defined using the extracted CDTire/3D model-dependent sensitive frequency subdomains, and the mean values of crucial data from vehicle ride comfort and durability simulations are used as constraints. A sequential-updating radial basis function metamodel-based optimisation method is adopted as the solver. The proposed method is employed to develop tyres for an SUV. Compared with typical road noise design indices-based static approximate optimisation approaches, the proposed method achieves a better overall optimisation effect. Using the developed CDTire/3D model, the road noise, ride comfort, and durability design indices of the SUV are improved by 9.8%, 6.7%, and 7.1%, respectively.
  Keywords: tyre optimisation design; vehicle multidisciplinary performance improvement; CDTire/3D model; virtual proving ground; metamodel technique.
  DOI: 10.1504/IJVD.2024.10062259
   
  
• Fuel-efficient predictive cruise control using the explicit MPC method for commercial vehicles  
  by Fawang Zhang, Jingliang Duan, Yuming Yin, Chunxuan Jiao, Genjin Xie, Congsheng Zhang, Shengbo Eben Li, Zhe Xin 
  Abstract: Fuel-efficient Predictive Cruise Control (FPCC) is of great significance in achieving fuel conservation. Model Predictive Control (MPC) serves as a promising method for the design of the FPCC controller. However, existing MPC-based FPCC controller on real vehicles remains challenging since MPC needs to find the optimal control law at each time step with limited computation time and resource. In this paper, we propose a learning-based explicit MPC method to learn the optimal policy of FPCC systems. We employ the neural network to approximate the policy, and transfer the online computation burden of the optimal control law to the offline policy training process. Simulations demonstrate that the method can effectively improve the real-time performance and generalize to different road topologies without sacrificing fuel economy and travel efficiency.
  Keywords: commercial vehicles; reinforcement learning; predictive cruise control; eco-driving.
  DOI: 10.1504/IJVD.2024.10062758
   
  

Special Issue on: New Energy Vehicles' NVH and Lightweight and Control Technologies

• Comparison of deep learning methods for predicting charging energy of power batteries  
  by Xuefeng Zhu, Guoliang Xie 
  Abstract: Accurate prediction of the electric vehicle charging energy is essential for power grid companies to rationally allocate power resources, customise appropriate tariffs and select the location of charging piles. Currently, machine learning methods have been widely applied in this field. Aiming to predict Electric Vehicles (EV) charging energy more precisely, this paper compares several machine learning methods and concludes that Long Short-Term Memory (LSTM) neural network has better behaviour. As the initial training data was incomplete, we supplemented the training data with MissFrorest neural network. We compared Back Propagation (BP), Xtreme Gradient Boosting (XGBoost), and LSTM networks for the prediction of charging energy, and found that LSTM has the best prediction effect, XGBoost has the second best, and BP has the worst effect. LSTM addresses the issue of gradient dispersion due to introducing time series, and thus has a better prediction effect. The experimental results show that Mean Absolute Error (MAE) and Root-mean-square error (RMSE) indices for four of five experimental vehicles using the LSTM algorithm are smaller than those using BP and XGBoost methods. Compared with the BP, XGBoost algorithms, the average reduction of MAE is 42.79%, 23.48%, and RMSE is reduced by 43.42%, 19.65%.
  Keywords: predicting charging energy; deep learning; power batteries; electric vehicles.
  
  

Special Issue on: Advanced Safety Design and Control for Electric Vehicles

• A new torque ripple suppression strategy based on the CSA for PMHM of electric vehicles under New European Driving Cycles  
  by Yao Zhang, Xiaodong Sun 
  Abstract: This paper presents a new torque ripple suppression strategy for a permanent magnet hub motor (PMHM) of electric vehicles' drive. With the complex characteristics such as nonlinear time delay and multi-dimension presented by the PMHM, the traditional PID controller has been unable to meet the requirements of the control system. Thus, the cat swarm algorithm (CSA) is introduced to improve the accuracy of PID parameters thanks to its good global search ability. Moreover, it is found that the proposed CSA-PID in the outer loop can obtain better performance such as smaller torque ripple and faster dynamic response both in steady and dynamic state compared with the traditional PID controller. Finally, the strategy proposed in this paper was applied to the vehicle model through HIL test platform. The possibility of applying the strategy proposed to EVs was verified under the New European Driving Cycle.
  Keywords: permanent magnet hub motor; cat swarm algorithm; torque ripple suppression; New European Driving Cycle.
  
  
• Research on crashworthiness and lightweight of frame body based on load path and material selection  
  by Tingting Wang, Ruoyan Dong, Yuechen Duan, Dongchen Qin 
  Abstract: In order to effectively optimise the frame body structure and match the performance of lightweight materials with the function of body structure, a material-structure optimisation framework of multi-material frame body is proposed to improve the lightweight and collision safety at the same time. Firstly, in order to improve the crashworthiness of the frame, the equivalent static load method is used to analyse the load path of the frame body to obtain the optimal structure. Secondly, the crashworthiness evaluation method based on evolutionary structural optimisation method is used to evaluate each member of frame body, which provides the basis for material selection. Finally, the material index is introduced to establish the material library. According to the deformation evaluation results, the material selection method based on bubbling method is used to select materials orderly to match the function of members, and the multi-material frame with the objective selection scheme is obtained. In this study, the proposed method is demonstrated by the lightweight of racing car body. The results show that the body mass is reduced by 25.60 kg after the lightweight design, and the crash safety is improved. Therefore, the proposed optimisation framework of multi-material frame body.
  Keywords: frame body; lightweight; load path; material selection.