The tire-road friction coefficient has a critical impact on the driving stability of vehicles, and it is the key parameter of vehicle dynamics control systems. This paper aims to improve the prediction accuracy and efficiency of the tire-road friction coefficient. Therefore, a Mind Evolutionary Algorithm optimized Back-Propagation (MEA-BP) neural network model for the prediction of the tire-road friction coefficient is proposed for tire-road friction coefficient predicting; and compared with the extreme learning machine (ELM) and BP neural network algorithms. The results show that the prediction accuracy rate of MEA-BP neural network algorithm is 8.8% higher than ELM algorithm, and 5.6% higher than BP neural network algorithm. In addition, different types and numbers of input variables are selected to study the efficiency and accuracy of tire-road friction coefficient prediction. The study found that the slip angle, tire longitudinal force, tire lateral force and tire vertical force have a significant impact on the prediction accuracy of the tire-road friction coefficient. As the number of input variables increases, the prediction accuracy gradually improves. When the number of input variables reaches 12, the growth rate of prediction accuracy slows down.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Mind Evolutionary Algorithm Optimized Back-Propagation Neural Network Model for Tire-Road Friction Coefficient Prediction


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jia, Limin (Herausgeber:in) / Easa, Said (Herausgeber:in) / Qin, Yong (Herausgeber:in) / Zhang, Fanhao (Autor:in) / Wu, Wenguang (Autor:in) / Tian, Shuangyue (Autor:in) / Xu, Menglong (Autor:in)

    Kongress:

    International Conference on SmartRail, Traffic and Transportation Engineering ; 2023 ; Changsha, China July 28, 2023 - July 30, 2023



    Erscheinungsdatum :

    14.08.2024


    Format / Umfang :

    19 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Road Friction Coefficient Prediction Method and Device for Each Tire

    YOON SANG HUN / LIM KI TAEG / SHIN DAE KYO et al. | Europäisches Patentamt | 2022

    Freier Zugriff


    Tire-road friction coefficient estimation with vehicle steering

    Hong, Sanghyun / Hedrick, J. Karl | IEEE | 2013


    Neural network based tire/road friction force estimation

    Matusko, J. / Petrovic, I. / Peric, N. | Tema Archiv | 2008