锂离子电池不析锂充电边界辨析化充电方法

doi:10.19595/j.cnki.1000-6753.tces.240440

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摘要开发不析锂的优化充电方法对于缓解电动汽车用户充电焦虑、里程焦虑具有重要意义。当前优化充电研究中涉及析锂边界的不同应用形式,但是它们之间的一致性和差异性尚不明晰,同时缺少易于工程应用的优化充电方法。围绕以上问题,该文进行了析锂边界不同应用形式的辨析和优化充电研究。首先,搭建包含析锂副反应的高精度电化学模型,在25℃和10℃下验证了仿真精度。其次,利用二分法和比例积分微分控制算法获取了不同应用形式的析锂边界,包括析锂电流边界、析锂电压边界和析锂在线边界,辨析了三者间的本质一致性和差异性。最后,基于析锂电压边界和遗传算法,优化得到兼顾充电时间和充电容量的五阶段恒流充电方法,进一步地,提出了四种五阶段恒流恒压充电方法,并分析了恒压阶段对于充电效果的影响。与传统恒流充电对比,所提出的优化充电方法可以大幅缩短充电时间,并最大化充电容量。该文阐明了析锂边界不同应用形式间的异同,提出的充电方法易于实现工程应用,同时为优化充电提供了新的研究思路。

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关键词 ：锂离子电池, 电化学模型, 析锂边界, 遗传算法, 优化充电

Abstract：The development of optimized charging methods without lithium plating is significant in alleviating electric vehicle users' charging and mileage anxiety. Current optimal charging research involves different forms of lithium plating boundaries. Still, their consistency and differences need to be clarified. In practice, a more theoretical basis is needed for choosing the application form of lithium plating boundaries. In addition, current lithium plating-free charging methods tend to switch the current according to the SOC, which makes the control process more complicated. Optimized charging methods should be explored for engineering applications.
Firstly, this paper defines different forms of lithium plating boundary. Specifically, the specific application of the lithium plating boundary is summarized into three forms: (1) The maximum charging current-SOC boundary, called “lithium plating current boundary”; (2) The threshold voltage-charging current boundary, “lithium plating voltage boundary”; (3) The maximum charging current-time curve obtained by combining PID and other control algorithms, “lithium plating online boundary”. A pseudo two dimensional (P2D) electro- chemical model with lithium plating side reactions is developed to obtain the lithium plating boundary. The model’s parameters are partly obtained from the manufacturer and partly identified by the adaptive particle swarm optimization algorithm. The accuracy of the model is verified at 25℃ and 10℃.
Secondly, three forms of lithium plating boundaries are obtained using dichotomous and proportional integral differential control algorithms. The lithium plating current boundary, lithium plating voltage boundary, and lithium plating online boundary are consistent. Through variable conversion, lithium plating current boundary and lithium plating voltage boundary can be derived from each other, only differing in application forms. The lithium plating online boundary can be transformed into the lithium plating current or voltage boundary. However, the lithium plating current or voltage boundary cannot be transformed into the lithium plating online boundary. It should be noted that the degree of consistency between the lithium plating current boundary and the lithium plating online boundary is affected by the control effect of the closed-loop controller. The consistency between the two boundaries is affected when the controller parameters are incorrect and the convergence time is too long.
Finally, a five-stage constant-current charging method that considers charging time and charging capacity is obtained based on the lithium plating voltage boundary and Genetic Algorithm. Compared with 1C, 2C, and 3C constant current charging, the charging time is reduced by 77.3%, 51.5%, and 22.9%, respectively. The charging capacity is 90.1%, 96.1%, and 101.8% of 1C, 2C, and 3C constant current charging. Four five-stage constant-current and constant-voltage charging methods are proposed, and the influence of the constant-voltage stage on the charging effect is analyzed. The constant voltage stage prolongs the charging time and increases the charging capacity. The smaller the minimum value of current set in the constant voltage stage, the longer the charging time and the larger the charging capacity. The proposed multi-stage constant current charging method switches the current according to the battery voltage without involving intermediate variables such as SOC, which is more straightforward to control and suitable for engineering applications.
The proposed charging method controls the charging process according to the voltage, which can easily be applied in engineering. The proposed multi-stage constant-current constant-voltage charging method can better balance the charging time and charging capacity, providing a new idea for the charging optimization of lithium-ion batteries.

Key words： Lithium-ion battery electrochemical model lithium plating boundary genetic algorithm optimal charging

收稿日期: 2024-03-19

PACS:

TM911

基金资助:国家自然科学基金资助项目（52177206）

通讯作者: 孙丙香女,1979年生,教授,博士生导师,研究方向为锂离子动力电池高效集成及智能管控技术。包括电池（组）建模与仿真、优化充电、特殊场景参数估计、热特性与低温加热策略、快速寿命预测、储能电池性能评估与经济性分析等。E-mail: bxsun@bjtu.edu.cn

作者简介: 马仕昌男,1997年生,博士研究生,研究方向为锂离子动力电池机理建模与优化充电技术。E-mail: 20117043@bjtu.edu.cn

引用本文:

孙丙香, 马仕昌, 陈昕, 张旭博, 张维戈. 锂离子电池不析锂充电边界辨析化充电方法[J]. 电工技术学报, 2025, 40(6): 1960-1973. Sun Bingxiang, Ma Shichang, Chen Xin, Zhang Xubo, Zhang Weige. Charging Boundary Analysis and Optimized Charging Method without Lithium Plating for Lithium-Ion Batteries. Transactions of China Electrotechnical Society, 2025, 40(6): 1960-1973.

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[1] 陈冲, 贾利民, 赵天宇, 等. 去碳化导向的轨道交通与新能源融合发展——形态模式、解决方案和使/赋能技术[J]. 电工技术学报, 2023, 38(12): 3321-3337.
Chen Chong, Jia Limin, Zhao Tianyu, et al.Decarbonization-oriented rail transportation and rene- wable energy integration development configurations, solutions, and enabling/empowering technologies[J]. Transactions of China Electrotechnical Society, 2023, 38(12): 3321-3337.
[2] 中国政府网: 政府工作报告[EB/OL]. (2024-03-12). https://www.gov.cn/yaowen/liebiao/202403/content_6939153.htm.
[3] 孙丙香, 宋东林, 阮海军, 等. 基于自产热和外传热的锂离子电池热学模型参数辨识方法[J]. 电工技术学报, 2024, 39(1): 278-288.
Sun Bingxiang, Song Donglin, Ruan Haijun, et al.Parameter identification method of thermal model of lithium-ion battery based on self-generated heat and external heat transfer[J]. Transactions of China Elec- trotechnical Society, 2024, 39(1): 278-288.
[4] 方斯顿, 刘龙真, 孔赖强, 等. 基于双向长短期记忆网络含间接健康指标的锂电池SOH估计[J]. 电力系统自动化, 2024, 48(4): 160-168.
Fang Sidun, Liu Longzhen, Kong Laiqiang, et al.State-of-health estimation for lithium-ion batteries incorporating indirect health indicators based on bi-directional long short-term memory networks[J]. Automation of Electric Power Systems, 2024, 48(4): 160-168.
[5] 刘素贞, 袁路航, 张闯, 等. 基于超声时域特征及随机森林的磷酸铁锂电池荷电状态估计[J]. 电工技术学报, 2022, 37(22): 5872-5885.
Liu Suzhen, Yuan Luhang, Zhang Chuang, et al.State of charge estimation of LiFeO₄ batteries based on time domain features of ultrasonic waves and random forest[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5872-5885.
[6] 孙丙香, 李凯鑫, 荆龙, 等. 锂离子电池不同工况下充电效果对比及用户充电方法选择研究[J]. 电工技术学报, 2023, 38(20): 5634-5644.
Sun Bingxiang, Li Kaixin, Jing Long, et al.Comparison of charging effect of lithium-ion battery under different working strategies and study on user charging method selection[J]. Transactions of China Electrotechnical Society, 2023, 38(20): 5634-5644.
[7] Tomaszewska A, Chu Zhengyu, Feng Xuning, et al.Lithium-ion battery fast charging: a review[J]. eTransportation, 2019, 1: 100011.
[8] Xie Wenlong, Liu Xinhua, He Rong, et al.Challenges and opportunities toward fast-charging of lithium-ion batteries[J]. Journal of Energy Storage, 2020, 32: 101837.
[9] Jiang Li, Huang Yuduo, Li Yong, et al.Optimization of variable-current charging strategy based on SOC segmentation for Li-ion battery[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(1): 622-629.
[10] 褚政宇. 基于降维电化学模型的锂离子动力电池无析锂快充控制[D]. 北京: 清华大学, 2019.
Chu Zhengyu.Fast charge control of lithium-ion power battery without lithium precipitation based on reduced-dimension electrochemical model[D]. Beijing: Tsinghua University, 2019.
[11] Purushothaman B K, Landau U.Rapid charging of lithium-ion batteries using pulsed currents[J]. Journal of the Electrochemical Society, 2006, 153(3): A533.
[12] Wu Xiaogang, Zhang Kun, Chen Yu, et al.Multistage fast charging optimization protocol for lithium-ion batteries based on the biogeography-based algo- rithm[J]. Journal of Energy Storage, 2022, 52: 104679.
[13] 龚浩然. 电动汽车锂离子动力电池优化充电策略研究[D]. 西安: 长安大学, 2019.
Gong Haoran.Research on optimal charging strategy of lithium-ion power battery for electric vehicle[D]. Xi’an: Changan University, 2019.
[14] Zhang Caiping, Jiang Jiuchun, Gao Yang, et al.Charging optimization in lithium-ion batteries based on temperature rise and charge time[J]. Applied Energy, 2017, 194: 569-577.
[15] 陈猛, 王军, 王雯雯, 等. 应用支持向量机的锂电池不可逆析锂检测研究[J]. 电工技术学报, 2025, 40(4): 1323-1332.
Chen Meng, Wang Jun, Wang Wenwen, et al.Research on irreversible lithium plating detection in lithium-ion batteries using support vector machine[J]. Transa- ctions of China Electrotechnical Society, 2025, 40(4): 1323-1332.
[16] 刘倩倩. 锂离子电池负极析锂机制及抑制方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2018.
Liu Qianqian.Study on the mechanism and inhibition method of lithium evolution from negative electrode of lithium-ion battery[D]. Harbin: Harbin Institute of Technology, 2018.
[17] 阮海军. 低温环境下锂离子电池优化加热及充电方法研究[D]. 北京: 北京交通大学, 2019.
Ruan Haijun.Study on optimal heating and charging method of lithium-ion battery in low temperature environment[D]. Beijing: Beijing Jiaotong University, 2019.
[18] 葛昊. 低温下抑制析锂的锂离子电池交流预热与快速充电方法[D]. 北京: 清华大学, 2017.
Ge Hao.Alternating current preheating and fast charging method for lithium ion battery for inhibiting lithium evolution at low temperature[D]. Beijing: Tsinghua University, 2017.
[19] 李慧芳, 陈荣, 韩江浩, 等. 一种锂离子电池快速充电策略的制定方法: CN112038719A[P].2020-12-04.
[20] Qin Yudi, Zuo Pengyu, Chen Xiaoru, et al.An ultra-fast charging strategy for lithium-ion battery at low temperature without lithium plating[J]. Journal of Energy Chemistry, 2022, 72: 442-452.
[21] 孙涛, 郑侠, 郑岳久, 等. 基于电化学热耦合模型的锂离子电池快充控制[J]. 汽车工程, 2022, 44(4): 495-504.
Sun Tao, Zheng Xia, Zheng Yuejiu, et al.Fast charging control of lithium-ion batteries based on electrochemical-thermal coupling model[J]. Auto- motive Engineering, 2022, 44(4): 495-504.
[22] Doyle M, Fuller T F, Newman J.Modeling of galvanostatic charge and discharge of the lithium/ polymer/insertion cell[J]. Journal of the Electro- chemical Society, 1993, 140(6): 1526-1533.
[23] Ma Shichang, Sun Bingxiang, Su Xiaojia, et al.Sensitivity analysis of electrochemical model para- meters for lithium-ion batteries on terminal voltages and anode lithium plating criterion[J]. Journal of Energy Storage, 2023, 71: 108127.
[24] He Zheng, Li Hang, Ji Weijie, et al.Investigation on Li-plating prevention optimal charging protocol of nickel-rich/graphite-SiO_x lithium ion battery[J]. Journal of Power Sources, 2023, 571: 233044.
[25] Wei Zhongbao, Yang Xiaofeng, Li Yang, et al.Machine learning-based fast charging of lithium-ion battery by perceiving and regulating internal microscopic states[J]. Energy Storage Materials, 2023, 56: 62-75.
[26] Jiang Li, Li Yong, Huang Yuduo, et al.Optimization of multi-stage constant current charging pattern based on Taguchi method for Li-ion battery[J]. Applied Energy, 2020, 259: 114148.
[27] 江凌峰, 龚邻骁, 金新宇, 等. 基于遗传算法的多模块IPOP双有源全桥DC-DC变换器总电流有效值优化策略[J]. 电工技术学报, 2023, 38(24): 6782-6797.
Jiang Lingfeng, Gong Linxiao, Jin Xinyu, et al.Total root mean square current optimization of IPOP dual active bridge DC-DC converter based on genetic algorithm[J]. Transactions of China Electrotechnical Society, 2023, 38(24): 6782-6797.
[28] 程翔鹏, 刘进军, 邵钰, 等. 峰值电流控制Buck变换器高频建模及结合遗传算法的控制器优化设计[J]. 电工技术学报, 2024, 39(1): 217-232.
Cheng Xiangpeng, Liu Jinjun, Shao Yu, et al.High-frequency modeling for peak current-model buck converters and optimal controller design by combining genetic algorithm[J]. Transactions of China Electrotechnical Society, 2024, 39(1): 217-232.