The climate-adaptive energy management system holds promising potential for harnessing the concealed energy-saving capabilities of connected plug-in hybrid electric vehicles. This research focuses on exploring the synergistic effects of artificial intelligence control and traffic preview to enhance the performance of the energy management system (EMS). A high-fidelity model of a multi-mode connected PHEV is calibrated using experimental data as a foundation. Subsequently, a model-free multistate deep reinforcement learning (DRL) algorithm is proposed to develop the integrated thermal and energy management (ITEM) system, incorporating features of engine smart warm-up and engine-assisted heating for cold climate conditions. The optimality and adaptability of the proposed system is evaluated through both offline tests and online hardware-in-the-loop tests, encompassing a homologation driving cycle and a real-world driving cycle in China with real-time traffic data. The results demonstrate that ITEM achieves a close to dynamic programming fuel economy performance with a margin of 93.7%, while reducing fuel consumption ranging from 2.2% to 9.6% as ambient temperature decreases from 15°C to -15°C in comparison to state-of-the-art DRL-based EMS solutions.


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    Titel :

    Integrated Thermal and Energy Management of Connected Hybrid Electric Vehicles Using Deep Reinforcement Learning


    Beteiligte:
    Zhang, Hao (Autor:in) / Chen, Boli (Autor:in) / Lei, Nuo (Autor:in) / Li, Bingbing (Autor:in) / Li, Rulong (Autor:in) / Wang, Zhi (Autor:in)

    Erscheinungsdatum :

    2023-08-28


    Anmerkungen:

    IEEE Transactions on Transportation Electrification (2023) (In press).


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629