International Journal of Vehicle Performance (8 papers in press)

Regular Issues

• Modelling of detailed vehicle dynamics and quantitative impact of electric motor placement on regenerative braking  
  by Shantanu Pardhi, Ajinkya Deshmukh, Hugo Ajrouche 
  Abstract: Using forward type vehicle simulation, this paper aims at comparing the potential and limitations of front wheel drive and rear wheel drive electric motor placements for regenerative braking under extreme driving situations. First, the considered dynamic/data-driven modelling approach for the complete traction chain with attention to the effects of detailed vehicle dynamics has been implemented in MATLAB Simulink. Simple parallel regenerative braking technique and recuperation favouring brake distribution strategies have been employed on a performance electric car example considering front and rear wheel propulsion cases. Powertrain behaviour in a dynamic driving scenario has been investigated to understand how the two cases with their corresponding recuperation favouring braking strategies perform under elevated transient vehicle dynamics. Finally, the impact of normal load transfer, tyre slip and wheel adhesion limits on regenerative braking has been quantitatively compared for the complete range of brake pedal demands using high-speed braking tests while avoiding wheel lock-up.
  Keywords: Regenerative braking; electric motor placement; vehicle dynamics; wheel slip; normal load transfer; powertrain modelling; brake bias strategy; FWD; RWD; simulation.
  DOI: 10.1504/IJVP.2022.10046859
   
  
• Performance investigation and energy optimisation in hybrid electric vehicle model using reinforcement learning and fuzzy controller  
  by Emmanuel Babu Pukkunnen, Neena M. Joseph, Bos Mathew Jos, Minu C. Joy, K.A. Eldhose 
  Abstract: Hybrid electric vehicles (HEVs) are considered as one of the prominent solutions in reducing vehicular emission. Batteries and internal combustion engines (ICE) are the important components of a HEV, which acts as primary and secondary power source respectively. They simplify the refuelling process by minimising fuel consumption and by reducing virulent emissions. In this research, a series-parallel drivetrain - HEV model is proposed for investigating the performance and energy optimisation of the HEVs. The model is trained to operate at near optimum efficiency for minimising the energy loss. A deep reinforcement learning and fuzzy logic controller based energy management approach is proposed to optimise the energy consumption in HEVs. Results show that the energy management system (EMS) of the model is controlled effectively by the deep reinforcement learning (DRL) algorithm. Effective speed control is achieved by fine tuning the parameters using a fuzzy based PID controller which can be validated from the simulation results.
  Keywords: HEVs; hybrid electric vehicles; series-parallel drivetrain; EMSs; energy management systems; DRL; deep reinforcement learning; fuzzy control logic; PID controllers; speed control.
  DOI: 10.1504/IJVP.2022.10048782
   
  
• Dual-clutch coordinating hill-start control strategy based on pseudospectral method  
  by Kegang Zhao, Haolong Zhong, Yanwei Liu, Jie Ye, Maoyu Mai 
  Abstract: An optimal framework for dual-clutch coordinating starting is presented to balance the friction work of two clutches. Based on the dual-clutch coordinating starting dynamics model, optimal equation is formulated in which the weighting of the friction work, square of the jerk, and square of the control variables are considered as the optimisation objectives, and the engine torque and clutch-torque change rate are considered as the control variables. The Legendre pseudospectral method is suitable to be used to solve complex nonlinear problems and it is adopted for transforming the optimal control problem into a nonlinear programming problem, and the sequential quadratic programming method is employed for solving the integral term constraint problem with a state variable. Using a dual-clutch transmission car as prototype, optimal trajectories for starting in different working conditions are obtained. Experiments performed on a dual-clutch coordinating starting bench test verify the effectiveness of proposed optimal control strategy.
  Keywords: vehicle engineering; dual-clutch transmission; coordinating hill-start; pseudo-spectral method; optimisation strategy.
  DOI: 10.1504/IJVP.2022.10047418
   
  
• Multi-objective control and energy management strategy based on deep Q-network for parallel hybrid electric vehicles  
  by Shiyi Zhang, Jiaxin Chen, Xiaolin Tang 
  Abstract: To promote the energy management strategy of hybrid electric vehicles towards the direction of intelligence, this paper proposes a control model to design the learning-based energy management strategy for a parallel hybrid electric vehicle, which uses the deep Q-network (DQN) algorithm of deep reinforcement learning to control the engine and continuously variable transmission (CVT) synchronously. After completing the offline training of the control model, the near-optimal control strategy fitted by the neural network parameters is saved, and it is loaded and tested directly during the online test, which can reflect if the neural network has learned the mapping relationship between a random state and the optimal action. The simulation result of the online test shows that the DQN-based EMS can achieve a fuel economy of 5.31L/100km, and the consuming time is 1.67s when running the testing cycle of 1686s, which can ensure the real-time application potential, adaptability, and robustness.
  Keywords: hybrid electric vehicle; multi-objective control; energy management strategy; deep reinforcement learning.
  DOI: 10.1504/IJVP.2022.10047568
   
  
• Design of variable ratio mathematical model for steer-by-wire system  
  by Ju Zhang, Xueyun Li, Jun Li 
  Abstract: In order to ensure the vehicle safety, improve the steering handling stability of vehicles on low adhesion road and at high speed, a variable transmission ratio mathematical model of steer-by-wire (SBW) system is proposed considering vehicle speed and road adhesion coefficient comprehensively. A characteristic simulation experiment is performed to obtain the yaw rate gain of the vehicle based on the vehicle model in CarSim. Then, the correction function of the ideal yaw rate gain is built based on the error variation rule between yaw rate gain and the ideal yaw rate gain. And then the curve of variable steering ratio is designed and modified. The influence of road adhesion coefficient on transmission ratio is analysed, and the function relationship between road adhesion coefficient, vehicle speed, and transmission ratio is built. Finally, the vehicle trajectory tracking simulation model is built in CarSim and MATLAB/Simulink to validate the effectiveness of the designed variable steering ratio function. The results show that the designed steering ratio function could calculate the reasonable steering ratio of the SBW system according to different vehicle speed and road adhesion coefficient. Comparing with the constant steering ratio, the variable steering ratio calculated by designed function could improve the lateral stability and the handling performance effectively on high-speed and low adhesion road.
  Keywords: SBW system; adhesion coefficient; handling performance; variable steering ratio.
  DOI: 10.1504/IJVP.2022.10045130
   
  
• Active steering control research using closed-loop dynamic simulation for semi-trailer trains  
  by Deng Zhaowen, Kong Xinxin, Yu Wei, Gao Wei 
  Abstract: To improve the lateral stability of the semi-trailer train at high speeds, the active steering control strategy for tractor and semi-trailer is proposed and examined using closed-loop dynamic simulation. A linear yaw plane model with 3-DOF of semi-trailer train is built and verified, and a directional preview driver model applied the preview-predictor follow strategy is developed and integrated with the vehicle model. The proportional feedforward, fuzzy feedback, and proportional-integral-derivative controller (PID) active steering controllers are proposed and adopted individually or in combination, the effectiveness of the active steering control strategy presented have been verified and analysed based on the driver/articulated vehicle closed-loop control system. The simulation results show that the active steering control strategy proposed has an obvious advantages over the conventional uncontrolled vehicle, effectively reducing the values of index, and improving the performance of high-speed lateral stability. The method has certain reference value for improving the active safety of articulated heavy vehicle.
  Keywords: active steering; proportional feedforward; fuzzy feedback; closed-loop system; preview-predictor; handling performance; roll stability.
  DOI: 10.1504/IJVP.2022.10047395
   
  
• Multi-modal user experience evaluation on in-vehicle HMI systems using eye-tracking, facial expression, and finger-tracking for the smart cockpit  
  by Wenbo Li, Yingzhang Wu, Guanzhong Zeng, Fan Ren, Mingqing Tang, Huafei Xiao, Yujing Liu, Gang Guo 
  Abstract: The trend toward intelligent connected vehicles (ICVs) led to numerous more novel and more natural human-vehicle relationships, which will bring about tremendous changes in smart cockpit functions and interaction methods. However, most in-vehicle human-machine interaction (HMI) systems focus on adding more functions, while few of them focus on the user experience (UX) of the system. This study presents an evaluation method of UX based on eye-tracking, finger movement tracking, and facial expression, the study also proposed a pleasantness prediction based on multi-layer perception (MLP) algorithm using multi-modal data. Through the UX experiment on two in-vehicle HMI systems, the study verified that the proposed evaluation method can be objective and efficient to evaluate the in-vehicle HMI system. Based on the MLP algorithm, the study trained the pleasantness prediction model using multi-modal data. Besides, we collected new data of the third in-vehicle HMI system to test the trained model and presented excellent test results.
  Keywords: HMI; human-machine interaction; user experience; driver emotion; behaviour analysis; smart cockpit.
  DOI: 10.1504/IJVP.2022.10045180
   
  
• Predictive ramp shift strategy with dual clutch automatic transmission combined with GPS and electronic database  
  by Chao Wang, Guangqiang Wu, Zhichao Lv, Xiang Zeng 
  Abstract: First, based on the two-parameter shift strategy, the ramp shift correction coefficient is added to get the ramp shift strategy. Back propagation (BP) neural network is used to establish the nonlinear mapping relationship among the gears, throttle opening, ramp and the ramp shift correction coefficient. Second, the road information is stored in the electronic database, the vehicle position information and the distance to the ramp are obtained through global positioning system (GPS), a predictive ramp shift strategy combining GPS and electronic database is proposed. The simulation results show that this strategy is helpful to eliminate the phenomenon of frequent shift and unexpected shift when the vehicle is driving on the ramp. Based on the experimental vehicle and the self-developed transmission control unit (TCU) hardware and software platform, the real vehicle test is carried out on the ramp road, and the results verified that the strategy is helpful to reduce the number of gears shifting, enhance the vehicle power performance and comfort when uphill.
  Keywords: ramp; shift strategy; BP neural network; GPS; global positioning system; electronic database; predictive.
  DOI: 10.1504/IJVP.2022.10047368