计算机科学
强化学习
水准点(测量)
控制器(灌溉)
PID控制器
人工智能
能源消耗
能源管理
启发式
控制工程
能量(信号处理)
工程类
统计
电气工程
生物
数学
大地测量学
地理
温度控制
农学
出处
期刊:IEEE Transactions on Transportation Electrification
日期:2022-09-01
卷期号:8 (3): 3275-3288
被引量:17
标识
DOI:10.1109/tte.2021.3132773
摘要
With the development of artificial intelligence, there has been a growing interest in machine learning-based control strategy, among which reinforcement learning (RL) has opened up a new direction in the field of hybrid electric vehicle (HEV) energy management. However, the issues of the current RL setting ranging from inappropriate battery state-of-charge (SOC) constraint to ineffective and risky exploration make it inapplicable to many industrial energy management strategy (EMS) tasks. To address this, an adaptive hierarchical EMS combining heuristic equivalent consumption minimization strategy (ECMS) knowledge and deep deterministic policy gradient (DDPG), which is a state-of-the-art data-driven RL algorithm, is proposed in this work. For comparison purposes, the proposed strategy is contrasted with dynamic programming (DP), proportion integration differentiation (PID)-based adaptive ECMS, and rule-based and standard RL-based counterparts, and the results show that the fuel consumption after SOC correction for the proposed strategy is very close to that of the DP-based control and lower than that of the other three benchmark strategies. Considering that the proposed strategy can make better use of the RL techniques while realizing an effective, efficient, and safe exploration in a data-driven manner, it may become a strong foothold for future RL-based EMS to build on, especially when the controller has to be trained directly and from scratch in a real-world environment.
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