电源学报

电池组不一致性参数估算方法

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陈智伟 ¹^,² , 张婷婷 ² , 张维戈 ¹^,² , 张言茹 ¹^,² , 张珺玮 ¹

电源学报 | 新能源发电与储能 2026,24(9): 196-204

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电源学报 |新能源发电与储能 2026 , 24 (9) : 196 -204

电池组不一致性参数估算方法

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陈智伟¹^,², 张婷婷², 张维戈¹^,², 张言茹¹^,², 张珺玮¹

作者信息

^1.北京交通大学国家能源主动配电网技术研发中心，北京　100044

^2.重载快捷大功率电力机车全国重点实验室，株洲　412000

通讯作者:

张维戈（1971—　），男，中国电源学会会员，通信作者，博士，教授。研究方向：动力电池应用技术，电力电子技术。E-mail：wgzhang@bjtu.edu.cn。

作者简介:

陈智伟（1999—　），男，硕士研究生。研究方向：锂离子动力电池健康状态估计研究。E-mail：22121439@bjtu.edu.cn。

张婷婷（1987—　），女，硕士，高级工程师。研究方向：轨道交通动力电池集成技术与安全性研究。E-mail：010200034378@crrcgc.cc。

张言茹（1990—　），女，博士，实验师。研究方向：动力电池成组技术。E-mail：yr_zhang@bjtu.edu.cn。

张珺玮（1996—　），女，博士研究生。研究方向：锂离子动力电池健康管理。E-mail：20117024@bjtu.edu.cn。

Battery Parameter Estimation Method Based on Simulation Database

Zhiwei CHEN¹^,², Tingting ZHANG², Weige ZHANG¹^,², Yanru ZHANG¹^,², Junwei ZHANG¹

Affiliations

^1.National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China

^2.National Key Laboratory of Heavy-haul, Express and High-power Electric Locomotives, Zhuzhou 412000, China

出版时间: 2026-09-30 doi: 10.13234/j.issn.2095-2805.2026.9.196

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摘要

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锂离子电池不一致性问题是影响电池组整体性能输出的关键因素之一，准确的不一致性参数估计对电池组的健康管理和优化控制至关重要。针对传统复杂电池单体参数估计算法拓展至电池组内所有单体应用时存在的计算量过大问题，提出了一种基于离线电池单体仿真数据库的不一致性参数估算方法。首先，通过搭建等效电路模型仿真生成综合的电池样本数据库；然后，从目标电池充电数据中提取片段电压特性曲线与数据库进行比对，基于相似性度量锁定最佳匹配样本，从而实现对组内所有电池单体容量及内阻的快速估算；接着，考虑到方法中插值误差及实际应用中电压采样误差的干扰，提出了基于窗口滑动搜索的修正机制，提高估计精度；最后，在实验室环境搭建的60个电池单体串联而成的电池组上进行了方法验证。结果表明，容量平均绝对百分比误差MAPE（mean absolute percentage error）不超过0.40%，内阻MAPE不超过2.60%，且在模拟采样误差的噪声数据上保持了较好的稳定性。

关键词

锂离子电池 / 不一致性参数估算 / 等效电路模型 / 电压特性曲线

Abstract

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The inconsistency problem of lithium-ion batteries is one of the key factors affecting the overall performance output of battery packs, and accurate estimation of inconsistency parameters is crucial for the health management and optimization control of battery packs. The traditional complex single battery cell parameter estimation algorithm faces the problem of huge computational complexity when extended to all cells within the battery pack. A method for estimating inconsistent parameters based on an offline single battery cell simulation database is proposed. First, by building an equivalent circuit model to simulate and generate a comprehensive battery sample database. Then, segment voltage characteristic curves are extracted from the target battery charging data and compared with the sample library. Based on similarity measurement, the best matching sample is locked, thereby achieving rapid estimation of the capacity and internal resistance of all battery cells in the group. Subsequently, considering the interference of interpolation errors in the method and voltage sampling errors in practical applications, a correction mechanism based on sliding window search is proposed to improve estimation accuracy. Finally, method validation was conducted on 60 single battery-cell series connected battery packs set up in a laboratory environment. The results showed that the mean absolute percentage error (MAPE) of the capacity did not exceed 0.40%, and MAPE of the internal resistance did not exceed 2.60%, and maintained good stability on simulated sampling error noise data.

Key words

Lithium-ion battery / inconsistency parameter estimation / equivalent-circuit model / voltage characteristic curve

引用本文

陈智伟, 张婷婷, 张维戈, 张言茹, 张珺玮. 电池组不一致性参数估算方法. 电源学报, 2026 , 24 (9) : 196 -204 . DOI: 10.13234/j.issn.2095-2805.2026.9.196

Zhiwei CHEN, Tingting ZHANG, Weige ZHANG, Yanru ZHANG, Junwei ZHANG. Battery Parameter Estimation Method Based on Simulation Database[J]. Journal of Power Supply, 2026 , 24 (9) : 196 -204 . DOI: 10.13234/j.issn.2095-2805.2026.9.196

基金

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重载快捷大功率电力机车全国重点实验室开放课题资助项目(KFKT2022-016)

参考文献

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文献

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参考文献引证文献

排序方式：

[1]

郭剑锋, 张雪美, 曹琪, 等. 电动汽车助力我国能源安全与“碳达峰、碳中和”协同推进[J]. 中国科学院院刊, 2024, 39(2): 397-407. Guo Jianfeng, Zhang Xuemei, Cao Qi, et al. Electric vehicles contribute to China’s energy security and carbon peaking and carbon neutrality[J]. Bulletin of Chinese Academy of Sciences, 2024, 39(2): 397-407. (in Chinese)

[2]

Zhang Junwei, Zhang Weige, Sun Bingxiang, et al. A novel method of battery pack energy health estimation based on visual feature learning[J]. Energy, 2024, 293: 130656.

[3]

侯健敏, 钱海峰, 李紫祥, 等. 基于三阶段参数辨识的锂离子电池SOC-SOH联合估计[J/OL]. 电源学报, 1-12 (2025-03-26) [2025-03-30]. https://link.cnki.net/urlid/12.1420.TM.20250326.1606.012. Hou Jianmin, Qian Haifeng, Li Zixiang, et al. Joint SOC-SOH estimation of lithium-ion batteries based on three-stage parameter identification[J/OL]. Journal of Power Supply, 1-12. (2025-03-26) [2025-03-30]. https://link.cnki.net/urlid/12.1420.TM.20250326.1606.012. (in Chinese)

[4]

梁叶, 蔡孟冶, 姜岩峰, 等. 基于修正安时积分法的锂电池荷电状态估算[J/OL]. 电源学报, 1-13. (2025-02-14) [2025-03-30]. https://link.cnki.net/urlid/12.1420.TM.20250214.1637.004. Liang Ye, Cai Mengye, Jiang Yanfeng, et al. Estimation of lithium battery state of charge based on the modified adams method[J/OL]. Journal of Power Supply, 1-13 (2025-02-14) [2025-03-30]. https://link.cnki.net/urlid/12.1420.TM.20250214.1637.004. (in Chinese)

[5]

彭自然, 王顺豪, 肖伸平, 等. 基于KA Informer的电动汽车动力电池荷电状态和健康状态估算[J]. 电工技术学报, 2025, 40(19): 6378-6394. Peng Ziran, Wang Shunhao, Xiao Shenping, et al. Estimation of EV power battery SOC & SOH based on KA informer[J]. Transactions of Electrical Engineering, 2025, 40(19): 6378-6394. (in Chinese)

[6]

陈智伟, 张维戈, 张珺玮, 等. 基于视觉特征的动力电池组综合健康评估及分筛方法[J]. 储能科学与技术, 2023, 12(7): 2211-2219. Chen Zhiwei, Zhang Weige, Zhang Junwei, et al. Comprehensive health assessment and screening method of power battery pack based on visual characteristics of charge curves[J]. Energy Storage Science and Technology, 2023, 12(7): 2211-2219. (in Chinese)

[7]

Jiang Yan, Jiang Jiuchun, Zhang Caiping, et al. A Copula-based battery pack consistency modeling method and its application on the energy utilization efficiency estimation[J]. Energy, 2019, 189: 116219.

[8]

范兴明, 吴润玮, 封浩, 等. 基于变窗口自适应无迹卡尔曼滤波的锂离子电池荷电状态预测[J]. 电工技术学报, 2025, 40(6): 1974-1983. Fan Xingming, Wu Runwei, Feng Hao, et al. Lithium-ion battery state of charge prediction based on variable-window adaptive unscented Kalman filter[J]. Transactions of China Electrotechnical Society, 2025, 40(6): 1974-1983. (in Chinese)

[9]

Zheng Yuejiu, Lu Languang, Han Xuebing, et al. LiFePO₄ battery pack capacity estimation for electric vehicles based on charging cell voltage curve transformation[J]. Journal of Power Sources, 2013, 226: 33-41.

[10]

Zhang Junwei, Zhang Weige, Zhang Yanru, et al. Capacity estimation for series-connected battery pack based on partial charging voltage curve segments[J]. Journal of Energy Storage, 2024, 95: 112576.

[11]

孙丙香, 庞俊峰, 苏晓佳, 等. 基于中低频阻抗谱的锂离子电池容量快速估计方法[J]. 中国公路学报, 2024, 37(2): 293-303. Sun Bingxiang, Pang Junfeng, Su Xiaojia, et al. A rapid capacity estimation method for lithium-ion batteries based on medium-low frequency electrochemical impedance spectroscopy[J]. China Journal of Highway and Transport, 2024, 37(2): 293-303. (in Chinese)

[12]

Zheng Yuejiu, Wang Jingjing, Qin Chao, et al. A novel capacity estimation method based on charging curve sections for lithium-ion batteries in electric vehicles[J]. Energy, 2019, 185: 361-371.

[13]

孙刚, 伍江江, 陈浩, 等. 一种基于切比雪夫距离的隐式偏好多目标进化算法[J]. 计算机科学, 2022, 49(6): 297-304. Sun Gang, Wu Jiangjiang, Chen Hao, et al. Hidden preference-based multi-objective evolutionary algorithm based on Chebyshev distance[J]. Computer Science, 2022, 49(6): 297-304. (in Chinese)

[14]

徐慧颖, 陈琦鹏, 刘耀辉, 等. 多分属性的非参数诊断分类: 18种距离判别法的对比[J]. 心理科学, 2023, 46(6): 1486-1494. Xu Huiying, Chen Qipeng, Liu Yaohui, et al. Nonparametric diagnostic classification for polytomous attributes: A comparison of 18 distance discriminant methods[J]. Journal of Psychological Science, 2023, 46(6): 1486-1494. (in Chinese)

[15]

吕晓俊. 基于等效电路模型的电池荷电状态估计方法综述[J]. 电子质量, 2024(6): 9-13. Lü Xiaojun. A review of battery charge state estimation methods based on equivalent circuit model[J]. Electronics Quality, 2024(6): 9-13. (in Chinese)

[16]

Tao Zhen, Zhao Zhenyu, Wang Changdong, et al. State of charge estimation of lithium batteries: Review for equivalent circuit model methods[J]. Measurement, 2024, 236: 115148.

[17]

王梦, 孙晓辉, 李景康, 等. 锂离子电池容量预测方法研究[J]. 电源技术, 2024, 48(11): 2201-2208. Wang Meng, Sun Xiaohui, Li Jingkang, et al. Research on lithium-ion battery capacity prediction methods[J]. Power Technology, 2024, 48(11): 2201-2208.

2026年第24卷第9期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.9.196

接收时间：2024-12-29
首发时间：2026-10-29
出版时间：2026-09-30

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出版历史

收稿日期：2024-12-29
修回日期：2025-02-08
录用日期：2025-02-24

基金

Open Project Program of National Key Laboratory of Heavy-haul, Express and High-power Electric Locomotive(KFKT2022-016)

重载快捷大功率电力机车全国重点实验室开放课题资助项目(KFKT2022-016)

作者信息

^1.北京交通大学国家能源主动配电网技术研发中心，北京　100044

^2.重载快捷大功率电力机车全国重点实验室，株洲　412000

通讯作者:

张维戈（1971—　），男，中国电源学会会员，通信作者，博士，教授。研究方向：动力电池应用技术，电力电子技术。E-mail：wgzhang@bjtu.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.9.196

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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