In energy management of HEVs, taking an energy distribution scheme derived from the given EMS will bring in changes of the vehicle state and driving state. Meanwhile, energy consumption of the powertrain occurs simultaneously with the transition of vehicle states. This instantaneous energy (or fuel) consumption and the sum of energy (fuel) it consumes over the future will provide a criterion for judging the strategy performance. Then, a new energy distribution scheme should be calculated according to the current vehicle states to accomplish the energy management. This described process contains main elements including the interaction of the decision maker with the controlled object and the environment it belongs to, the policy (or strategy), states, actions, and costs (or rewards). Because the state transition process of the vehicle shows a distinct Markovian property [54], we will model and formulate the HEV energy management problem based on MDP theory. The general modeling part will be described in this chapter, while the similarities and differences of the modeling process for different energy management problems will be described in the relevant sections of subsequent chapters.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Background: Deep Reinforcement Learning


    Beteiligte:
    Li, Yuecheng (Autor:in) / He, Hongwen (Autor:in)


    Erscheinungsdatum :

    01.01.2022


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Making a failure scenario using adversarial reinforcement learning background

    WACHI AKIFUMI | Europäisches Patentamt | 2022

    Freier Zugriff


    Powerslide Control with Deep Reinforcement Learning

    Jaumann, Florian / Schuster, Tobias / Unterreiner, Michael et al. | Springer Verlag | 2024

    Freier Zugriff

    Deep Reinforcement Learning for IoT Interoperability

    Klöser, Sebastian / Kotstein, Sebastian / Reuben, Robin et al. | TIBKAT | 2021


    Deep Reinforcement Learning For Secure Communication

    Yang, Yinchao / Shikh-Bahaei, Mohammad | IEEE | 2022