Considering physical constraints in online optimization and training safety is a challenge for the implementation of the deep reinforcement learning (DRL) algorithm. Especially for the nonlinear system, the mapping relationship between the output action of the agent and the control signals is difficult to obtain. This article proposes a novel DRL framework for online optimization in energy management of a power-split hybrid electric vehicle (HEV), which combines a neural network (NN)-based multiconstraints optimal strategy and a rule-based-restraints system (RBRS). The proposed method named reward-directed policy optimization (RDPO) adopts the exterior point method (EPM) and curriculum learning (CL) to direct the agent to recognize and avoid irrational control signals and optimize the fuel economy. The energy management strategy (EMS) considering fuel consumption minimization and irrational control signals’ avoidance is optimized by training the agent through the world light vehicle test cycle (WLTC). A competitive fuel economy, 4.495 L/100 km, is achieved with no irrational control signals. Based on the online adaptability evaluation conducted, the fuel consumption of the vehicle under the New European Driving Cycle (NEDC) and the China Typical Urban Driving Cycle (CTUDC) has been reduced to 4.113 L/100 km and 3.221 L/100 km, respectively, with no irrational control signals. The superiority in optimization, calculation efficiency, and safety is verified through comparisons with various DRL agents.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Safe Deep Reinforcement Learning-Based Constrained Optimal Control Scheme for HEV Energy Management


    Beteiligte:
    Liu, Zemin Eitan (Autor:in) / Zhou, Quan (Autor:in) / Li, Yanfei (Autor:in) / Shuai, Shijin (Autor:in) / Xu, Hongming (Autor:in)


    Erscheinungsdatum :

    2023-09-01


    Format / Umfang :

    5482786 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deep Reinforcement Learning-based Optimal Time-constrained Intercept Guidance

    Sinha, Abhinav / White, Devin / Cao, Yongcan | AIAA | 2024



    Safe Deep Reinforcement Learning for Adaptive Cruise Control by Imposing State-Specific Safe Sets

    Brosowsky, Mathis / Keck, Florian / Ketterer, Jakob et al. | IEEE | 2021


    Safe Locomotion Within Confined Workspace using Deep Reinforcement Learning

    Dastider, Apan / Raza, Sayyed Jaffar Ali / Lin, Mingjie | IEEE | 2021


    ISUAM: Intelligent and Safe UAM with Deep Reinforcement Learning

    Garcia, Cristiano P. / Weigang, Li / Hirata, Nina S. T. et al. | IEEE | 2023