Forthcoming and Online First Articles

International Journal of Vehicle Performance

International Journal of Vehicle Performance (IJVP)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Vehicle Performance (3 papers in press)

Regular Issues

  • Exploration of robust and intelligent navigation algorithms to ensure off-road autonomous vehicle mobility   Order a copy of this article
    by Michael Cole, Kumar B. Kulkarni, Jordan Ewing, Seth Tau, Christopher Goodin, Paramsothy Jayakumar 
    Abstract: The combat capabilities development command (DEVCOM) ground vehicle systems centre (GVSC) is supporting unmanned ground vehicle (UGV) development. Past experimentations of a military UGV demonstrated that its autonomous mode performed worse than the tele-operated mode. To address this, a systematic investigation into path planners for military vehicles in off-road environments was executed. A UGV simulator was used to evaluate vehicle and planner performance through a range of obstacle avoidance scenarios in deformable soil to capture the effects of vehicle-terrain interactions across multiple soil types. Monte Carlo methods were used to evaluate the robustness of five path planners ranging from classical to state-of-the-art planners, with normally-distributed variability in environmental and vehicle initial conditions. After running thousands of simulations, results show how each algorithm compares to one another in several key metrics including overall success rates. These results will help inform decisions in future military UGV path planner selection.
    Keywords: navigation algorithms; vehicle mobility; autonomous vehicles; unmanned ground vehicles; UGV; path planning; vehicle-terrain interactions; VTI.
    DOI: 10.1504/IJVP.2024.10062615
     
  • Optimal adaptive Lipschitz continuous sliding mode controller with APSO algorithm for an autonomous vehicle   Order a copy of this article
    by Ayoub Belkheir, El Mehdi Mellouli, Vidas Žuraulis 
    Abstract: he paper presents a method for controlling lateral movement with uncertain and unknown dynamics for an autonomous vehicle that combines the adaptive particle swarm optimisation (APSO) algorithm and the Lipschitz continuous sliding mode controller (LCSMC). Designing the desired trajectory, and modelling the vehicle using the bicycle model is the initial step. The control law is defined using the classical Lipschitz continuous sliding mode control (LCSMC) in the following step. In the next step, the systems nonlinear functions are modelled using a radial basis function neural network (RBFNN), the developed intelligent triangular observer is proposed, and the suggested approach adaptive Lipschitz continuous sliding mode control (ALCSMC) is suggested with stability analysis using the Lyapunov function and optimising the parameters gain using APSO algorithm after. Finally, the study is finished by simulating this strategy to see their performance and effectiveness in accomplishing the desired results.
    Keywords: adaptive particle swarm optimisation; APSO; autonomous vehicle; Lipschitz continuous sliding mode control; LCSMC; Lyapunov function; triangular observer.

  • A systematic approach to design electric vehicle powertrains: model-based simulation and real-world application   Order a copy of this article
    by Shafi Md. Istiak, Md. Rashed Hossain, Nahin Tasmin 
    Abstract: In this age of electric vehicle (EV) technology, simulating the vehicle system cuts prototyping expenses and aids engineers in choosing the appropriate components and systems for the vehicle. This study presents a methodology for developing a mathematical model for electric vehicles using CarSim 2017 Software, which lacks an inherent electric vehicle model but can be connected to MATLAB Simulink. Additionally, the research applies the insights derived from simulation results to guide the conversion of a combustion engine vehicle into an electric one. The process starts with creating a simulation model in MATLAB/Simulink that integrates a permanent magnet synchronous motor (PMSM), motor controller, and battery with a vehicle model in CarSim software. Based on simulation results, appropriate powertrain specifications were generated for an EV prototype developed by converting a conventional vehicle. The prototype was tested in real-world conditions using a standard driving cycle to evaluate its performance.
    Keywords: electric vehicle; MATLAB; electric powertrain; Simulink; CarSim; simulation; prototyping.