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Online Learning Control for Hybrid Electric Vehicle

Chinese Journal of Mechanical Engineering 2012年第1期25卷 98-106页

作者： LI Weimin XU Guoqing XU Yangsheng Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina

Improvements in hybrid electric vehicle (HEV) fuel economy and emissions heavily depend on an efficient energy management strategy (EMS). However, the uncertainty of future driving conditions generally cannot be easily tackled in EMS design. Most existing EMSs act upon fixed parameters and cannot adapt to varying driving conditions. Therefore, they usually fail to fully explore the potential of these advanced vehicles. In this paper, a novel EMS design procedure based on neural dynamic programming (NDP) is proposed. The NDP is a generic online learning algorithm, which combines stochastic dynamic programming (SDP) and the temporal difference (TD) method. Instead of computing the utility function and optimal control actions through Bellman equations, the NDP algorithm uses two neural networks to approximate them. The weights of these neural networks are updated online by the TD method. It avoids the high computational cost that SDP suffers from and is suitable for real-time implementation. The main advantages of NDP EMS is that it does not rely on prior information related to future driving conditions, and can self-tune with a wide variance in operating conditions. The NDP EMS has been applied to “Qianghua-I”, a prototype of a parallel HEV, using a revolving drum test bench for verification. Experiment results illustrate the potential of the proposed EMS in terms of fuel economy and in keeping state of charge (SOC) deviations at a low level. The proposed research ensures the optimality of NDP EMS, as well as real-time applicability.

关键词： hybrid electric vehicle neural dynamic programming energy management strategy

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State of the Art of Adaptive dynamic programming and Reinforcement Learning

引用

CAAI Artificial Intelligence Research 2022年第2期1卷 93-110页

作者： Derong Liu Mingming Ha Shan Xue Department of Mechanical and Energy Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Electrical and Computer Engineering University of Illinois at ChicagoIL 606071USA School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China

This article introduces the state-of-the-art development of adaptive dynamic programming and reinforcement learning(ADPRL).First,algorithms in reinforcement learning(RL)are introduced and their roots in dynamic programming are *** dynamic programming(ADP)is then introduced following a brief discussion of dynamic *** in ADP and RL have enjoyed the fast developments of the past decade from algorithms,to convergence and optimality analyses,and to stability *** key steps in the recent theoretical developments of ADPRL are mentioned with some future *** particular,convergence and optimality results of value iteration and policy iteration are reviewed,followed by an introduction to the most recent results on stability analysis of value iteration algorithms.

关键词： adaptive dynamic programming approximate dynamic programming adaptive critic designs neuro-dynamic programming neural dynamic programming reinforcement learning intelligent control learning control optimal control

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