International Journal of Vehicle Design (12 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.
  
  
• A Novel Prediction and Evaluation Method for Vehicle Stability  
  by Bo Wang, Wenyi Huang, Bing Lu, Jiajun Li, Chao Sun 
  Abstract: The vehicle stability performance is of great significance during the vehicle product development and electronic control system parameter calibration, especially the transient motion process of the vehicle. Most previous evaluation methods are mainly vehicle performance indexes, such as sideslip angle. Since the vehicle motion is a transient process from the tyre to the vehicle, in this paper, a wheel characteristic index namely the polymerisation degree of tyre steering centres is tried to predict vehicle stability earlier. Two calculation methods for steering centre including the particle motion trajectory method and the rigid body kinematics method are developed and compared. The polymerisation degree of tire steering centres is proposed and applied to the evaluation and prediction of vehicle stability. The simulation results show that the proposed index can evaluate the vehicle transient handling performance more sensitively, thus reserving more time for vehicle stability control with potential.
  Keywords: tyre steering centre; vehicle stability; transient handling performance; prediction and evaluation; polymerisation degree of tyre steering centres; rigid body kinematics.
  DOI: 10.1504/IJVD.2024.10061409
   
  
• A novel nonlinear observer for inertial parameters of lightweight electric vehicle through adaptive dual unscented Kalman filter  
  by Xianjian Jin, Zhaoran Wang, Zhiwei Li, Zeyuan Yan, Guodong Yin 
  Abstract: The potential performance of the foregoing vehicle active safety control systems heavily depends on accurate knowledge of important vehicle states and inertial parameters. This paper proposes a novel nonlinear observer that adaptive dual unscented Kalman filters (ADUKF) operates in parallel to synchronously estimate LEV inertial parameters and fundamental states including the vehicle mass, yaw moment of inertia, as well as vehicle velocity, vehicle sideslip angle. The observer only uses real-time measurements from torque information, angular velocity from in-wheel motor and other multi-sensors, such as steering wheel angle, lateral acceleration in a standard car. The LEV dynamics estimation model considering payload variations is established, local observability of the ADUKF observer is analysed and derived via Lie and differential geometry theory. Simulations verify the effectiveness of developed ADUKF observer compared with dual unscented Kalman filter (DUKF) and dual extended Kalman filter (DEKF).
  Keywords: lightweight electric vehicles; nonlinear observer; inertial parameter; unscented Kalman filter; adaptive estimation.
  DOI: 10.1504/IJVD.2024.10061413
   
  
• Sensor-fusion-based road friction estimation for robust safety-critical trajectory planning of automated driving  
  by Liang Shao, HuangSong Chen, Jun Liu, Hesheng Tang 
  Abstract: Road friction estimation methods can be divided into vehicle dynamics method (VDM) and camera based method (CBM). This paper proposes a framework to estimate road friction coefficient (RFC) by combing VDM and CBM on dry/wet road condition, considering camera misclassification and driving excitation accurate detection, and then applies this RFC for robust safety-critical trajectory planning of automated driving. Firstly, in terms of VDM, the RFC is estimated with a stable nonlinear estimator based on robust excitation detection. Then, RFC from VDM and CBM are fused considering camera mis-classification. The estimation of RFC are subsequently applied for safety critical trajectory planning with two-stage model predictive control (MPC). Simulations based on Carsim demonstrate that, the proposed estimation framework can better guarantee planning safety than CBM and VDM combined method without considering camera mis-classification or reliable excitation detection.
  Keywords: road friction estimation; sensor fusion; trajectory planning; two-stage model predictive control.
  DOI: 10.1504/IJVD.2024.10061479
   
  
• Energy management and dynamic control for skid-steered unmanned special vehicle with range-extended system  
  by Dequan Zeng, Yiming Hu, Zhenwen Deng 
  Abstract: In order to achieve the fast mobility of skid-steered unmanned special vehicle with range-extended system, the extended range system model is explored and established, the economic optimal operating point matching the power performance requirements is extracted, and the energy management strategy with feedforward-feedback tracking power controller is designed. Aiming at addressing the problem that the dynamic control effect is easily deteriorated due to parameter uncertainty and actuator saturation in vehicle motion system, the anti-saturation feedforward-feedback control law is explored and developed based on Lyapunov method. The results of simulation and real vehicle test show that, the designed energy management strategy has satisfied transient and steady-state power tracking performance, and the designed dynamic control strategy has satisfied transient and steady-state handling tracking performance, which meets the requirements of rapid and stable maneuverability for the skid-steered unmanned special vehicle with extended range system.
  Keywords: energy management; dynamic control; unmanned special vehicle; anti-saturation feedforward-feedback control law; Lyapunov method.
  DOI: 10.1504/IJVD.2024.10061629
   
  
• Designing a simulation framework to develop and evaluate an eco-routing strategy  
  by Yiqun Xia, Vincent Quast, Xuejing Luo, Lutz Eckstein 
  Abstract: This paper uses a novel simulation framework to develop and evaluate an eco-routing strategy, which aims to optimise the vehicle route. The simulation framework consists of three blocks: A microscopic traffic model simulates various traffic scenarios; a traffic management centre is responsible for processing transient traffic data; analytical powertrain models represent longitudinal dynamics of real vehicles. The route optimization is formulated as a constrained optimisation problem and solved by the new open-source software OR-Tools. In addition, the effectiveness of the eco-routing strategy is evaluated based on three vehicle types. Vehicle trajectories on different roadways are quantified by key performance indicators to understand how the eco-route reduces energy demand compared with alternative routes. According to the network-wide simulation results, the average energy-saving effect of the eco-route compared to the fast-route is 10.35%, while the effect is smaller than the short-short with an energy saving of 5.01%.
  Keywords: eco-routing; energy demand prediction; traffic simulation; powertrain simulation.
  DOI: 10.1504/IJVD.2024.10062217
   
  
• 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
   
  
• Path tracking and stability control of 4WID electric vehicles based on variable prediction horizon MPC  
  by Dongmei Wu, Qiang Zhang, Changqing Du, Yang Li 
  Abstract: This study presents a coordinated strategy for path-tracking based on variable prediction horizon model predictive control (VPH MPC) and stability control. First, the model predictive control (MPC) with different prediction horizons is used for the path-tracking controller. The effect of prediction horizons on path-tracking error and vehicle stability is analyzed. On the above basis, an adaptive path-tracking controller is proposed, where the desired acceleration is restricted and the prediction horizon is regulated based on the vehicle stability. Then, a DYC method is integrated with the path-tracking controller to further improve the vehicle's stability and path-tracking capability. The coordinated control algorithm is first validated through simulation and then the path-tracking controller with different prediction horizons is tested on a real vehicle. The simulation outcomes demonstrate that the path-tracking capability and stability can be improved significantly under extreme conditions. The real-vehicle experiments validate the effect of various prediction horizons on path-tracking capability.
  Keywords: path tracking; stability control; 4WID electric vehicle; variable prediction horizon; model predictive control.
  
  
• 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.