The invention provides a hybrid electric vehicle ecological driving strategy based on multi-agent reinforcement learning, which is different from an ecological driving strategy based on traditional reinforcement learning, and a multi-agent cooperative training framework is innovatively designed, so that decoupling processing of upper-layer speed planning and lower-layer energy management can be effectively avoided, and the reliability of the hybrid electric vehicle is improved. The global collaborative optimization is realized, and a better energy-saving effect can be obtained. Compared with the prior art, the strategy provided by the invention has the advantages that the calculation time is greatly shortened, and better real-time performance is provided in control.

    本发明提供了一种基于多智能体强化学习的混合动力汽车生态驾驶策略,其不同于基于传统强化学习的生态驾驶策略,创新性地设计了多智能体协同训练框架,从而能够有效避免上层速度规划和下层能量管理解耦处理,实现了全局协同优化,并可获取更优的节能效果。本发明的策略相比现有技术大幅缩减了计算时间,在控制中提供了较好的实时性。


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    Titel :

    Hybrid electric vehicle ecological driving strategy based on multi-agent reinforcement learning


    Weitere Titelangaben:

    一种基于多智能体强化学习的混合动力汽车生态驾驶策略


    Beteiligte:
    LI JIAQI (Autor:in) / HE HONGWEN (Autor:in) / WANG YONG (Autor:in) / WU JINGDA (Autor:in) / WANG PEI (Autor:in)

    Erscheinungsdatum :

    10.01.2025


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06V




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