Forthcoming and Online First Articles

International Journal of Vehicle Performance

International Journal of Vehicle Performance (IJVP)

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International Journal of Vehicle Performance (9 papers in press)

Regular Issues

  • Structural optimisation design of impact resistant composite wheel with compression/injection molding hybrid structure   Order a copy of this article
    by Yizhuo Wang, Yingchun Shan, Duo Xu, Xiandong Liu 
    Abstract: Based on the advantages of continuous fibre reinforced and long fibre reinforced injection moulding composite wheels, a composite wheel with pressure injection hybrid structure for passenger cars is designed. First, based on the equivalent static load method, the topology of the wheel spokes is optimised under the 13 degree impact dynamics condition, and the initial structure of the wheel is obtained. Then, by analysing the stress state and failure mode under the impact load, the stiffness matching design of the spoke section is carried out to determine the location of the layer and the material ratio. Finally, the parameters of the wheel are optimised, and the final structure of the wheel is obtained. The results show that the optimised wheel can pass the 13-degree impact test, and the weight of the optimised wheel is reduced from 4.61kg to 3.921kg.
    Keywords: hybrid structure; stiffness matching; impact resistance; optimisation design.

  • Investigation of regenerative braking for the electric mining truck based on fuzzy control   Order a copy of this article
    by Denghao Luo, Weiwei Yang, Yilin Wang, Yijun Han, Qiang Liu, Yaodong Yang 
    Abstract: This paper proposes a braking force distribution strategy to control regenerative braking by analysing the vehicle’s state so that regenerative braking does not damage the battery while recovering energy. In this paper, considering the particular characteristics of regenerative braking, a braking force distribution strategy for the front and rear axles of the vehicle is designed so that the rear axle can obtain more braking force safely. After that, a real-time optimal control strategy is proposed for regenerative braking. It uses a fuzzy control algorithm to allocate the proportion of regenerative braking by considering the battery capacity, braking intensity, and vehicle speed. The proposed strategy can recover energy while protecting the battery as much as possible. Simulation results show that the pure electric vehicle using the fuzzy control algorithm can reduce the allocation ratio of regenerative braking when the battery capacity is high, or the vehicle speed and braking intensity are high, while recovering as much energy as possible under reasonable circumstances. Therefore, the proposed regenerative braking control strategy in this paper can meet the regenerative braking requirements of pure electric vehicles.
    Keywords: electric mining truck; regenerative braking; fuzzy control; energy management.
    DOI: 10.1504/IJVP.2023.10057687
  • Optimisation study of aerodynamic drag based on flow field topology in box-type trucks   Order a copy of this article
    by Zihou Yuan, Wangyang Xiang, Hongwei Zhang, Zigang Zhao 
    Abstract: The research objective of this study is to solve the problem of excessive air resistance of box trucks at high speed. Taking the small box-type truck model as the object of study, the simulations with CFD software to simulate the external flow field of the small box-type truck driving at high speed. Under the premise of ensuring driving safety, the area at the front of the truck and the top of the carriage is identified as the area to be optimised. The local optimum criteria are used to topologise the local external flow field to be optimised and the model is remodelled according to the optimisation results. Validation showed that a suitable optimisation scheme could be proposed by using the local optimum criteria. The design method provided in this scheme can provide a better scheme for the research of truck drag reduction, improve design efficiency and save optimisation time.
    Keywords: truck; CFD simulation; local optimum criteria; flow field topology; Tosca fluid; local external flow field.
    DOI: 10.1504/IJVP.2023.10058001
  • A new adaptive second-order non-singular terminal sliding mode lateral control combined with neural networks for autonomous vehicle   Order a copy of this article
    by Moussa Abdillah, El Mehdi Mellouli 
    Abstract: This paper presents a novel adaptive second-order non-singular terminal sliding mode control combined with a radial basic neural network structure and a triangular neural observer to model and control an autonomous vehicle. Firstly, the dynamics of the vehicle are presented. Secondly, the control strategy is designed, more precisely unmodelled dynamics of the system are estimated by using artificial neural networks to model them and inject them into the control law taking into account the system stability by using the Lyapunov function. Thirdly, as it is not always possible to access all the state variables representing the system, an auxiliary dynamic system, called an observer combined with the neural networks is used, to estimate the state of some dynamic variables of the vehicle which are basically not measurable. Lastly, the efficiency and superiority of the proposed method are proved by simulation results performed using MATLAB.
    Keywords: terminal sliding mode control; radial basic neural network; RBNN; triangular neural observer; autonomous vehicle; Lyapunov function.
    DOI: 10.1504/IJVP.2024.10059306
  • Torsional vibration analysis and optimisation of a hybrid vehicle powertrain   Order a copy of this article
    by Bowen Ruan, Guangqiang Wu 
    Abstract: Aiming at a dual-motor torque coupling hybrid vehicle, its powertrain model is established by using the lumped mass method, and an engine transient torque model based on cylinder pressure is established to analyse its natural characteristics and torsional vibration response under typical working modes. Then, the sensitivity analysis of the main parameters of the powertrain such as flywheel inertia is carried out. Based on the analysis results, the multi-objective genetic algorithm is used to optimise the parameters of the powertrain. The results show that the optimised parameters can reduce the peak second-order angular acceleration of the transmission input shaft by 25.93% and 8.06%, and reduce the peak second-order angular acceleration of the driving wheel by 32.72% and 35.14% in two working modes.
    Keywords: hybrid; powertrain; torsional vibration; multi-objective genetic algorithm; multi-mode; simulation; parameter optimisation.
    DOI: 10.1504/IJVP.2023.10055648
  • Active variable geometry suspension system development for a small off-road vehicle   Order a copy of this article
    by Khashayar Moridpour, Mohammad Hasan Shojaeefard, Masoud Masih-Tehrani, Rambod Yahyaei 
    Abstract: In this paper, a non-linear full-vehicle model of a mini Baja off-road vehicle is retrofitted with a series active variable geometry suspension (SAVGS). Small off-road vehicles usually have trouble performing tight cornering maneuvers due to loss of tire normal force generation and grip, leading to poor steering performance. The maneuverability is of paramount importance for race cars, thus increasing the front axle roll stiffness by changing spring forces could lead to oversteer behavior which is desired. The SAVGS can improve the cornering performance of the off-road vehicle. A powerful multibody dynamics model is proposed for vehicle and suspension dynamics behavior simulation. Overly, a configuration of 45 degrees left (outer) link and 150 degrees right (inner) link produced the best results. The proposed SAVGS satisfies the stability in the high LATAC cornering test, contrary to the conventional vehicle, and improves the understeer gradient between 15% to 58% in the other cases.
    Keywords: off-road vehicles; multibody dynamics simulation; variable geometry suspension; understeer gradient; active suspension; mini Baja; SAVGS; cornering maneuvers; steering performance; race cars.
    DOI: 10.1504/IJVP.2023.10055702
  • Objectification and prediction of the subjective criticality of axle damages using artificial neural networks as well as multibody- and real-time simulations   Order a copy of this article
    by Robert Schurmann, Alexander Lion, Bernhard Schick, Philipp Rupp 
    Abstract: For the assessment of axle damages, real vehicle tests have mostly been used so far, but they are dangerous and difficult to reproduce. Therefore, driving simulators are becoming increasingly important for the virtual rating of vehicles. Regardless of whether a real vehicle or a driving simulator is used, the prediction of the subjective perception of axle damages requires time-consuming driving tests. A powerful dynamic driving simulator is used to obtain subjective evaluations of various axle damages. Objective vehicle quantities are logged simultaneously. Subsequently, multilinear regression (MLR) models and artificial neural networks (ANN) are used to identify correlations and predict subjective evaluations based on objective data. Furthermore, real-time capable vehicle models in CarMaker and multibody dynamic (MBD) models in ADAMS/Car are used to virtually carry out driving manoeuvres and generate synthetic data. By combining the simulated vehicle data with an ANN, subjective driver evaluations can be predicted entirely virtual.
    Keywords: ANN; artificial neural network; axle damage; correlation; driving simulator; multibody simulation; MLR; multilinear regression; objective metrics; realtime simulation; subjective assessments; vehicle stability.
    DOI: 10.1504/IJVP.2023.10055674
  • Driving authority allocation model for human-machine co-driving system considering fault tolerant control   Order a copy of this article
    by Jinli Xie, Xiaojun Huang, Licheng Li 
    Abstract: To improve driving safety of the human-machine co-driving (HMC) vehicles, an HMC system with a dynamic authority allocation model is extended in the base of the parallel shared steering control system. The proposed system consists of a compensation control loop, a driver model control loop, and an automatic controller control loop. These three control loops can be coupled using the dynamic authority allocation model. According to the status of the driver and the HMC system, the authority allocation model dynamically changes the authority level of each control loop and coordinates each control loop to complete the driving task. The effectiveness of the extended system is verified by simulation. The results show that the impacts of different driving characteristics and states, interferences, and controller faults on the system can be weakened, and the full load working time of the trajectory tracking controller is reduced.
    Keywords: human-machine co-driving system; redundancy; safety margin; fault tolerant control.
    DOI: 10.1504/IJVP.2023.10055650
  • Twin delayed deep deterministic reinforcement learning application in vehicle electrical suspension control   Order a copy of this article
    by Daoyu Shen, Shilei Zhou, Nong Zhang 
    Abstract: Coming with the rising focus of the driving comfort request, more efforts are being delivered into the study of suspension system. Comparing with other traditional control methods, the machine learning control strategy has demonstrated its optimality in dealing with different class of roads. The work presented in this paper is to apply twin delayed deep deterministic policy gradients (TD3) in suspension control which enables suspension controller to go beyond searching for an optimal set of system parameters from traditional control method in dealing with different class of pavements. To achieve this, a suspension model has been established together with a reinforcement learning algorithm and an input signal of pavement. The performance of the twin delayed reinforcement agent is compared against deep deterministic policy gradients (DDPG) and deep Q-learning (DQN) algorithms under different types of pavement. The simulation result shows its superiority, robustness and learning efficiency over other reinforcement learning algorithms.
    Keywords: vehicle vertical vibration; suspension system control; artificial intelligence; reinforcement learning; twin delayed deep deterministic policy (TD3); neural network design.
    DOI: 10.1504/IJVP.2023.10055649