Intelligent Energy Management Strategy Based on an Improved Reinforcement Learning Algorithm With Exploration Factor for a Plug-in PHEV

An intelligent energy management strategy (EMS) based on an improved Reinforcement Learning (RL) algorithm is developed to enhance the adaptability of the EMS and to further improve the fuel efficiency of a Plug-in Parallel Hybrid Electric Vehicle (PHEV). Both the numerical model and the energy mana...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-07, Vol.23 (7), p.8725-8735
Main Authors: Lin, Xinyou, Zhou, Kuncheng, Mo, Liping, Li, Hailin
Format: Article
Language:English
Subjects:
Algorithms
Batteries
Divergence
Dynamic programming
Energy efficiency
Energy management
energy management strategy
Engines
Fuel consumption
Fuel economy
Hybrid electric vehicle
Hybrid electric vehicles
Machine learning
Markov chains
Numerical models
Optimal control
Real-time systems
reinforcement learning algorithm
self-adaptive exploration factor
State of charge
Torque
Transition probabilities
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Summary:An intelligent energy management strategy (EMS) based on an improved Reinforcement Learning (RL) algorithm is developed to enhance the adaptability of the EMS and to further improve the fuel efficiency of a Plug-in Parallel Hybrid Electric Vehicle (PHEV). Both the numerical model and the energy management strategy of a plug-in PHEV are described. The improved RL with Q-learning algorithm is implemented to acquire the optimal control strategies for improving fuel economy. The Markov Chain is employed to calculate the Transition Probability Matrix of the required power. A Kullback-Leibler (KL) divergence rate is designed to activate the update of EMS, when a new corresponding driving cycle is expected. An Exploration Factor (EF) is proposed to overcome the disadvantages of the normal RL algorithm in convergence rate and reward cost evaluation. The diverse KL divergence rates are examined to seek optimal solutions. The normal-RL strategy, rule-based strategy, and dynamic programming strategy are implemented as benchmark strategies to verify the effectiveness of the proposed strategy. The validation results indicate that the improved RL algorithm with EF makes it possible to promote the EMS capable of significantly improving the energy efficiency of a plug-in PHEV.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2021.3085710