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Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus

机译：串联并联插电式混合动力公交车的连续控制策略和交通信息的能源管理深度强化学习

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摘要

Hybrid electric vehicles offer an immediate solution for emissions reduction and fuel displacement under the current technique level. Energy management strategies are critical for improving fuel economy of hybrid electric vehicles. In this paper we propose a energy management strategy for a series-parallel plug-in hybrid electric bus based on deep deterministic policy gradients. Specifically, deep deterministic policy gradients is an actor-critic, model-free reinforcement learning algorithm that can assign the optimal energy split of the bus over continuous spaces. We consider that the buses are driving in a fixed bus line, where driving cycle is constrained by the traffic. The traffic information and number of passengers are also incorporated into the energy management system. The deep reinforcement learning based energy management agent is trained with a large amount of driving cycles that generated from traffic simulation. Experiments on the traffic simulation driving cycles show that the proposed approach outperforms conventional reinforcement learning approach and exhibits performance close to the global optimal dynamic programming. Moreover, it also has great generality to the standard driving cycles that are significantly different with the ones that it has been trained with. We also show some interesting attributes of learned energy management strategies through visualizations of the actor and critic. The main contribution of this study is to explore the incorporation of traffic information within hybrid electric vehicle energy managment through advanced intelligent algorithms.
机译：在当前技术水平下，混合动力电动汽车为减少排放和减少燃料排放提供了立即解决方案。能源管理策略对于提高混合动力汽车的燃油经济性至关重要。在本文中，我们提出了基于深度确定性策略梯度的串联-并联插电式混合动力电动客车的能量管理策略。具体来说，深度确定性策略梯度是一种行为准则，无模型的强化学习算法，可以为连续空间分配公交车的最佳能量分配。我们认为公交车是在固定的公交线路上行驶，而公交线路受交通流量的限制。交通信息和乘客人数也被合并到能源管理系统中。基于深度强化学习的能源管理代理通过交通模拟生成的大量驾驶周期进行训练。在交通模拟驾驶周期上的实验表明，所提出的方法优于传统的强化学习方法，并具有接近于全局最优动态规划的性能。此外，它对标准驾驶周期也具有很大的通用性，与所接受的标准驾驶周期明显不同。我们还通过演员和评论家的可视化展示了所学能源管理策略的一些有趣属性。这项研究的主要贡献是通过先进的智能算法探索交通信息在混合动力电动汽车能源管理中的整合。

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  • 来源
    《Applied Energy》 |2019年第1期|454-466|共13页
  • 作者

    Wu Yuankai; Tan Huachun; Peng Jiankun; Zhang Hailong; He Hongwen;

  • 作者单位

    Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China|Univ Wisconsin Madison & Southeast Univ, Joint Res Inst Internet Mobil, Nanjing 211102, Jiangsu, Peoples R China;

    Southeast Univ, Sch Transportat, Nanjing 211102, Jiangsu, Peoples R China|Univ Wisconsin Madison & Southeast Univ, Joint Res Inst Internet Mobil, Nanjing 211102, Jiangsu, Peoples R China;

    Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China|Univ Wisconsin Madison & Southeast Univ, Joint Res Inst Internet Mobil, Nanjing 211102, Jiangsu, Peoples R China;

    Southeast Univ, Sch Transportat, Nanjing 211102, Jiangsu, Peoples R China|Univ Wisconsin Madison & Southeast Univ, Joint Res Inst Internet Mobil, Nanjing 211102, Jiangsu, Peoples R China;

    Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China|Beijing Inst Technol, Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Energy management; Hybrid electric vehicle; Deep reinforcement learning; Deep deterministic policy gradient;

    机译：能源管理;混合动力电动汽车;深度强化学习;深度确定性策略梯度;

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