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An indirect reinforcement learning based real-time energy management strategy via high-order Markov Chain model for a hybrid electric vehicle

机译：一种间接加固基于混合电动车辆的高阶马车链模型的实时能源管理策略

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摘要

This paper proposes a real-time indirect reinforcement learning based strategy to reduce the fuel consumption. In order to improve the real-time performance and achieve learning online, the simulated experience from environment model is adopted for the learning process, which is called indirect reinforcement learning. To establish an accurate environment model, a high-order Markov Chain is introduced and detailed, which is more precise than a widely used first-order Markov Chain. Corresponding with the model, how the reinforcement learning algorithm learns from the simulated experience is illustrated. Furthermore, an online recursive form of the transition probability matrix is derived, through which the statistical characteristics from the practical driving conditions can be collected. The induced matrix norm is chosen as a criterion to quantify the differences between the transition probability matrices and to determine the time for updating the environment model and triggering the recalculation of the reinforcement learning algorithm. Simulation results demonstrate that, compared with the direct RL, the proposed strategy can effectively reduce the learning time while maintains satisfied fuel economy. Furthermore, a hardware-in-the-loop experiment verifies its real-time capability and actual applicability. (c) 2021 Elsevier Ltd. All rights reserved.
机译：本文提出了基于实时间接强化学习的策略，以降低燃料消耗。为了改善实时性能并在线实现学习，学习过程采用了环境模型的模拟体验，称为间接增强学习。为了建立准确的环境模型，介绍了一款高阶马尔可夫链，并详细说明，比广泛使用的一阶马尔可夫链更精确。与模型相对应，如何从模拟体验中汲取加强学习算法。此外，推导出过渡概率矩阵的在线递归形式，可以收集来自实际驾驶条件的统计特性。选择诱导的矩阵规范作为量化过渡概率矩阵之间的差异并确定更新环境模型的时间并触发加强学习算法的重新计算的准则。仿真结果表明，与直接RL相比，该策略可以有效地减少学习时间，同时保持满足的燃料经济性。此外，硬件循环实验验证了其实时能力和实际适用性。 （c）2021 elestvier有限公司保留所有权利。

著录项

  • 来源
    《Energy》 |2021年第2期|121337.1-121337.16|共16页
  • 作者

    Yang Ningkang; Han Lijin; Xiang Changle; Liu Hui; Li Xunmin;

  • 作者单位

    Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China|Natl Key Lab Vehicular Transmiss Beijing 100081 Peoples R China;

    Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China|Natl Key Lab Vehicular Transmiss Beijing 100081 Peoples R China|Beijing Inst Technol Adv Technol Res Inst Jinan Jinan 250000 Peoples R China;

    Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China|Natl Key Lab Vehicular Transmiss Beijing 100081 Peoples R China;

    Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China|Natl Key Lab Vehicular Transmiss Beijing 100081 Peoples R China|Beijing Inst Technol Adv Technol Res Inst Jinan Jinan 250000 Peoples R China;

    China North Vehicle Res Inst Beijing 100072 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Hybrid electric vehicle; Real-time energy management; Indirect reinforcement learning; High-order Markov chain;

    机译：混合动力电动车;实时能源管理;间接加固学习;高阶马尔可夫链;

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