Reinforcement Learning-Based Electric Vehicles Energy Management Strategy with Battery Thermal Model

• Published in: 东华大学学报（英文版） Vol. 40; no. 1; pp. 80 - 87
• Main Authors: HUANG Gan, CAO Tongjie, HAN Junhua, ZHAO Ping, ZHANG Guanglin
• Format: Journal Article
• Language: English
• Published: College of Information Science and Technology,Donghua University,Shanghai 201620,China%China Information Technology Designing&Consulting Destitute Co.,Ltd.,Beijing 100048,China%Zhengyuan Geomatics Group Co.,Ltd.,Beijing 101300,China 2023
• Subjects: energy management; electric vehicle(EV); battery thermal management; reinforcement learning
• ISSN: 1672-5220
• DOI: 10.19884/j.1672-5220.202111001

TM921.5; The promotion of electric vehicles(EVs)is restricted due to their short cruising range.It is desirable to design an effective energy management strategy to improve their energy efficiency.Most existing work concerning energy management strategies focused on hybrids rather than the EVs.The work focusing on the energy management strategy for EVs mainly uses the traditional optimization strategies,thereby limiting the advantages of energy economy.To this end,a novel energy management strategy that considered the impact of battery thermal effects was proposed with the help of reinforcement learning.The main idea was to first analyze the energy flow path of EVs,further formulize the energy management as an optimization problem,and finally propose an online strategy based on reinforcement learning to obtain the optimal strategy.Additionally,extensive simulation results have demonstrated that our strategy reduces energy consumption by at least 27.4%compared to the existing methods.