In recent years, with the development of wheel-side motors and hub motors, distributed electric drive vehicles, gradually enter the electric vehicle market.Tire force are often derived from rule-based model in the past. However, distributed electric drive vehicles have a higher degree of freedom put forward new control requirements. This puts forward higher requirements for the accuracy of the tire force model. Rule-based model cannot meet the requirements quite well. Because of this, our study established a tire force residual correction framework for distributed electric drive vehicles. The framework consists of a neural network model (MLP, MLP-seq, and MLP-mixer) and a physical rule-based model. The framework was proved in the study to output a more accurate force estimation which will help dynamic modeling and control tasks.


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    Titel :

    A Data-Driven Framework for Tire Force Estimation of Distributed Electric-Drive Vehicles


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Mastinu, Giampiero (Herausgeber:in) / Braghin, Francesco (Herausgeber:in) / Cheli, Federico (Herausgeber:in) / Corno, Matteo (Herausgeber:in) / Savaresi, Sergio M. (Herausgeber:in) / Yan, Rujun (Autor:in) / Jiang, Kun (Autor:in) / Zhang, Bowei (Autor:in) / Miao, Jinyu (Autor:in) / Yang, Diange (Autor:in)

    Kongress:

    Advanced Vehicle Control Symposium ; 2024 ; Milan, Italy September 01, 2024 - September 05, 2024



    Erscheinungsdatum :

    04.10.2024


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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