A Self-adaptive Energy Management Strategy for Plug-in Hybrid Electric Vehicle based on Deep Q Learning. (June 2020)

• Record Type: Journal Article
• Authors: Zou, Runnan; Zou, Yuan; Dong, Yanrui; Fan, Likang
• Is Part Of: Journal of physics. Volume 1576(2020)
• Publication Date: 2020-06
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1576/1/012037
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: With the development of energy management, deep learning-based algorithm has become a widely concerned strategy. The presetting of neural network is deemed as a key of effectiveness of the method. For the purpose of improving fuel economy of plug-in hybrid electric vehicle (PHEV) based on the deep Q learning, an self-adaptive energy management strategy is proposed in this paper. In order to obtain an optimal learning rate which is one of the key hyper parameter for deep Q network, deep Q learning (DQL) with normalized advantage function (NAF) and genetic algorithm (GA) is combined together. The improvement of optimized learning rate is verified by comparing optimized learning rate with different other learning rates. Simulation results proves the optimized learning rate achieves the best improves fuel economy of PHEV compared with other sets of learning rate. The result indicates the effectiveness of GA in finding an optimal hyper parameter and the effectiveness GA-NAF-DQL in fuel saving in PHEV.