This paper introduces a novel approach for dynamically detecting the lowest speed threshold at which regenerative braking is effective in electric vehicles (EVs). The control approach is based on real-time sensing of the motor controller dc link current and disabling regenerative braking when current changes direction while the motor is operating as a generator. Various factors influencing the regenerative braking capability of EVs at low speed are discussed, and the simulation studies are carried out to illustrate the effect of each factor on the displacement of the low-speed threshold. Based on the results obtained from the simulation studies, a dynamic low-speed cutoff point (LSCP) detection method is proposed. This method requires no hardware modification to the vehicle braking architecture and can be implemented solely by modifying the brake controller. The proposed method is tested on an experimental EV test platform for a predetermined drive cycle. It is shown that in comparison to considering a constant low-speed threshold during braking, the amount of energy recaptured through the regenerative braking process can be improved by taking advantage of the proposed method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Maximizing Regenerative Braking Energy Recovery of Electric Vehicles Through Dynamic Low-Speed Cutoff Point Detection


    Contributors:


    Publication date :

    2019-03-01


    Size :

    3343005 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Regenerative braking for electric vehicles

    Wyczalek, F.A. / Wang, T.C. | Tema Archive | 1992


    Regenerative braking for electric vehicles

    Wyczalek,F.A. / Wang,T.C. / FW Lilly,US et al. | Automotive engineering | 1992


    Modeling Designed for Regenerative Braking Energy Recovery of Pure Electric Commercial Vehicles

    Yandong, Feng / Zhicheng, Wei / Chunjiang, Meng et al. | IEEE | 2024