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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摘要目前传统电池充电方法中,充电效率与安全运行矛盾显著,且电池组充电过程缺乏单体不一致性控制,导致容量浪费。为此,该文提出一种锂离子电池快充-均衡双层优化控制方法。首先,建立模型描述电池的电气特性和热学特性。其次,基于细分混合启发式鲸鱼优化算法对单体电池充电曲线进行优化,以最大电压、电流、温度为约束,以充电速度为优化目标,实现安全快速充电。与传统充电方法相比,充电时间可减少124 s,充电速度提高6.84%,且工作温度小于安全温度。再次,结合被动均衡拓扑提出电池组充电-均衡一体化方法,并基于强化学习对充均策略进行优化,以能量损失、充电速度、不一致性为约束条件,实现电池组快速充电与高效均衡的综合最优。最后,与传统方法相比,可降低70%~90%电压差,并提高电池组2%~4%可用容量,有效解决了电压不一致性带来的容量浪费问题。

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关键词 ：锂离子电池, 快速充电优化, 充电策略, 强化学习

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

收稿日期: 2024-08-20

PACS:

TM912

基金资助:国家自然科学基金重点项目（62333013）、国家自然科学基金优秀青年科学基金项目（62122041）、国家自然科学基金区域创新发展联合基金项目（U24A20159）、国家自然科学基金项目（62403292）、山东省杰出青年基金项目（ZR2021JQ25）和山东省自然科学基金项目（ZR2022QF138）资助

通讯作者: 商云龙男,1984年生,博士,教授,主要研究方向为储能电池安全高效管理与控制。E-mail: yshang@sdu.edu.cn

作者简介: 毛路男,1999年生,硕士研究生,研究方向为锂离子电池加热、均衡与电池管理系统设计。E-mail: maolu@mail.sdu.edu.cn

引用本文:

毛路, 赵文元, 王越, 朱瑞, 商云龙. 锂离子电池快充-均衡双层优化控制方法[J]. 电工技术学报, 2025, 40(19): 6395-6406. Mao Lu, Zhao Wenyuan, Wang Yue, Zhu Rui, Shang Yunlong. Double Layer Optimal Control Method of Fast Charge Equalization for Lithium Ion Batteries. Transactions of China Electrotechnical Society, 2025, 40(19): 6395-6406.

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