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Article

An Energy Management Strategy for Hybrid Energy Storage System Based on Reinforcement Learning

1
School of Information Science & Technology, University of Science and Technology of China, Hefei 230027, China
2
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230027, China
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2023, 14(3), 57; https://doi.org/10.3390/wevj14030057
Submission received: 26 January 2023 / Revised: 19 February 2023 / Accepted: 21 February 2023 / Published: 24 February 2023
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)

Abstract

Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy.
Keywords: hybrid energy storage system; energy management strategy; system modeling; speed prediction; reinforcement learning hybrid energy storage system; energy management strategy; system modeling; speed prediction; reinforcement learning

Share and Cite

MDPI and ACS Style

Wang, Y.; Li, W.; Liu, Z.; Li, L. An Energy Management Strategy for Hybrid Energy Storage System Based on Reinforcement Learning. World Electr. Veh. J. 2023, 14, 57. https://doi.org/10.3390/wevj14030057

AMA Style

Wang Y, Li W, Liu Z, Li L. An Energy Management Strategy for Hybrid Energy Storage System Based on Reinforcement Learning. World Electric Vehicle Journal. 2023; 14(3):57. https://doi.org/10.3390/wevj14030057

Chicago/Turabian Style

Wang, Yujie, Wenhuan Li, Zeyan Liu, and Ling Li. 2023. "An Energy Management Strategy for Hybrid Energy Storage System Based on Reinforcement Learning" World Electric Vehicle Journal 14, no. 3: 57. https://doi.org/10.3390/wevj14030057

APA Style

Wang, Y., Li, W., Liu, Z., & Li, L. (2023). An Energy Management Strategy for Hybrid Energy Storage System Based on Reinforcement Learning. World Electric Vehicle Journal, 14(3), 57. https://doi.org/10.3390/wevj14030057

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