锂离子电池快充-均衡双层优化控制方法

doi:10.19595/j.cnki.1000-6753.tces.241481

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Abstract The optimization of the charging curve is of great significance for the safe and long-life operation of the battery. A secure, fast, and intelligent charging method is crucial to improving the working efficiency of the battery pack and delaying the aging of the battery pack. Currently, most charging optimization algorithms focus on a single battery. However, battery packs in electric vehicles comprise hundreds or thousands of cells in practical applications. Therefore, it is necessary to optimize the overall charging strategy of the battery pack based on the optimization of the single battery charging curve. Two major problems exist in the traditional charging methods: the contradiction between charging efficiency and safe operation; capacity waste due to the imbalance of the single battery during the charging process of the battery pack. This paper proposes a double-layer optimization control method for rapidly charging and equalizing lithium-ion batteries based on the actual demand trigger. This method first optimizes the charging curve of a single battery based on the subdivision hybrid heuristic whale optimization algorithm. Then, the charging imbalance of the battery pack is optimized based on reinforcement learning and passive equalization topology. The first step is to establish a second-order RC equivalent circuit model and an equivalent thermal model to describe the electrical and thermal characteristics of the battery. The niching hybrid heuristic whale optimization algorithm (NHWOA) optimizes the MCC charging curve of the single battery. The optimization method takes the maximum voltage, current, and temperature as constraints and the charging speed as the optimization goal. Compared with the traditional CC-CV charging, this method reduces the total time by 124 s, the maximum temperature rise by 1.19℃, effectively shortens the charging time, and significantly reduces the core temperature during charging.
A passive equalization topology is designed, and the unbalanced charging problem of the battery pack is optimized with a Q-learning reinforcement learning algorithm. Taking energy loss, charging speed, and inconsistency as constraints, the optimization method trains the battery charging Q-learning algorithm. The results show that the method significantly reduces the voltage inconsistency of the battery pack by 88.9% at most and increases the SOC of the battery pack by 7.77% at most, realizing the comprehensive optimization of fast charging and efficient equalization of the battery pack. Finally, an experimental platform for real-time online charging control of the battery pack is built. Compared with the traditional charging method, the charging speed of this method is increased by 6.84%, the maximum temperature rise is reduced by 2.71%, and the operating temperature is lower than the safe temperature. Compared with the traditional battery pack charging method, the voltage difference is reduced by 70%~90%, and the usable capacity of the battery pack is increased by 2% to 4%. The real-time online control method of battery pack charging can improve the charging speed of the battery, reduce the core temperature of the battery during charging, and improve the inconsistency of battery voltage and the usable capacity of the battery pack.

Key words： Lithium ion battery fast charging optimization charging strategy reinforcement learning

Received: 20 August 2024

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TM912

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	Mao Lu
	Zhao Wenyuan
	Wang Yue
	Zhu Rui
	Shang Yunlong

Cite this article:

Mao Lu,Zhao Wenyuan,Wang Yue等. Double Layer Optimal Control Method of Fast Charge Equalization for Lithium Ion Batteries[J]. Transactions of China Electrotechnical Society, 2025, 40(19): 6395-6406.

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