A predictive energy management strategy considering travel route information is proposed to explore the energy-saving potential of plug-in hybrid electric vehicles. The extreme learning machine is used as a short-term speed predictor, and the battery temperature is added as an optimization term to the cost function. By comparing the training data sets, it is found that using the real-world historical speed information for training can achieve higher prediction accuracy than using typical standard driving cycles. The speed predictor trained based on the data considering travel route information can further improve the prediction accuracy. The impact of battery temperature on the total cost is also analyzed. By adjusting the temperature weighting coefficient of the battery, a balance between economy and battery aging can be achieved. In addition, it is found that the ambient temperature also affects vehicular energy consumption. Finally, the proposed method is compared with PMP, MPC, and CD-CS methods, showing its effectiveness and practicability.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Naturalistic Data-Driven Predictive Energy Management for Plug-In Hybrid Electric Vehicles


    Beteiligte:
    Tang, Xiaolin (Autor:in) / Jia, Tong (Autor:in) / Hu, Xiaosong (Autor:in) / Huang, Yanjun (Autor:in) / Deng, Zhongwei (Autor:in) / Pu, Huayan (Autor:in)


    Erscheinungsdatum :

    01.06.2021


    Format / Umfang :

    4903468 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A data-driven predictive energy management strategy for plug-in hybrid vehicles

    Lohrer, Jurgen / Forth, Matthias / Lienkamp, Markus | IEEE | 2017


    Energy management strategies for plug-in hybrid electric vehicles

    Gonder,J. / Markel,T. / National Renewable Energy Lab.,US | Kraftfahrwesen | 2007


    Energy Management Strategies for Plug-In Hybrid Electric Vehicles

    Gonder, J. / Markel, T. / Society of Automotive Engineers | British Library Conference Proceedings | 2007


    Data-Driven Reinforcement Learning–Based Real-Time Energy Management System for Plug-In Hybrid Electric Vehicles

    Qi, Xuewei / Wu, Guoyuan / Boriboonsomsin, Kanok et al. | Transportation Research Record | 2016


    Energy Management Strategies for Plug-In Hybrid Electric Vehicles

    Gonder, Jeffrey / Markel, Tony | SAE Technical Papers | 2007