Further Idea on Optimal Q-Learning Fuzzy Energy Controller for FC/SC HEV

Jili Tao (), Ridong Zhang () and Yong Zhu ()
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Jili Tao: NingboTech University, School of Information Science and Engineering
Ridong Zhang: Hangzhou Dianzi University, The Belt and Road Information Research Institute
Yong Zhu: NingboTech University, School of Information Science and Engineering

Chapter Chapter 10 in DNA Computing Based Genetic Algorithm, 2020, pp 261-274 from Springer

Abstract: Abstract With the development of intelligent algorithms, the learning-based algorithm has been considered as viable solutions to various optimization and control problems. GA can also be efficient to optimize the new emerging intelligent algorithm. Here, an adaptive fuzzy energy management control strategy (EMS) based on Q-Learning algorithm is presented for the real-time power split between the fuel cell and supercapacitor in the hybrid electric vehicle (HEV) in order to adapt the dynamic driving pattern and decrease the fuel consumption. Different from the driving pattern recognition based method, Q-Learning controller observes the driving states, takes actions, and obtains the effects of these actions. By processing the accumulated experience, the Q-Learning controller progressively learns an appropriate fuzzy EMS output tuning policy that associates suitable actions to the different driving patterns. The environment adaptation capability of fuzzy EMS is then improved needless of driving pattern recognition. To enhance the learning capability and decrease the effect on the initial values of Q-table, GA can also be utilized to optimize the initial values of Q-Learning based fuzzy energy management.

Date: 2020
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DOI: 10.1007/978-981-15-5403-2_10

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