电源学报

基于三阶段参数辨识的锂离子电池SOC-SOH联合估计

PDF下载

侯健敏 ¹^,² , 钱海峰 ¹ , 李紫祥 ¹ , 杨文静 ¹

电源学报 | 新能源发电与储能 2026,24(9): 132-144

收起

电源学报 |新能源发电与储能 2026 , 24 (9) : 132 -144

基于三阶段参数辨识的锂离子电池SOC-SOH联合估计

全屏

侯健敏¹^,², 钱海峰¹, 李紫祥¹, 杨文静¹

作者信息

^1.南京信息工程大学自动化学院，南京　210044

^2.江苏省输配电装备技术重点实验室，常州　213022

通讯作者:

钱海峰（2000—　），男，通信作者，硕士研究生。研究方向：电动汽车充电安全。E-mail：2659353390@qq.com。

作者简介:

侯健敏（1978—　），女，博士，副教授。研究方向：锂电池状态评估，综合能源系统预测与优化调度。E-mail：jmhou@nuist.edu.cn。

李紫祥（2000—　），男，硕士研究生。研究方向：综合能源系统优化调度。E-mail：1659301644@qq.com。

杨文静（1999—　），女，硕士研究生。研究方向：综合能源系统优化调度。E-mail：2497732960@qq.com。

Joint SOC-SOH Estimation of Lithium-ion Batteries Based on Three-stage Parameter Identification

Jianmin HOU¹^,², Haifeng QIAN¹, Zixiang LI¹, Wenjing YANG¹

Affiliations

^1.College of Automation Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

^2.Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Changzhou 213022, China

出版时间: 2026-09-30 doi: 10.13234/j.issn.2095-2805.2026.9.132

文章导航

摘要

收起

锂离子电池模型参数的精确辨识对电池剩余电量的观测和使用寿命的延长起关键作用。但是，传统参数辨识方法常因参数耦合、陷于局部最优等因素影响估计精度，导致后续联合估计的准确度降低。为此，提出了基于三阶段参数辨识的荷电状态-健康状态SOC-SOH（state of charge-state of health）联合估计。一阶段根据电池内部动态特性构建RC模型快慢动态2个模块；二阶段在快动态FD（fast dynamics）模块采用遗忘因子递推最小二乘法FFRLS（forgetting factor recursive least squares）自适应辨识参数R₀、R₁、C₁；三阶段在慢动态SD（slow dynamics）模块采用自适应扩展卡尔曼滤波AEKF（adaptive extended Kalman filter）算法进行在线参数辨识，并结合斑马智能优化算法ZOA（zebra optimization algorithm）寻优，解决局部最优问题，进一步提高参数U_OC、R₂、C₂的辨识准确度。考虑到电池容量与电池状态随时间变化的不同特性，采用多时间尺度对SOC-SOH联合估计，提高估计精度。仿真结果表明，基于改进后的联合估计算法在不同温度、不同工况下对SOC估计的平均误差及最大误差均小于传统算法，具有良好的估计特性。

关键词

斑马智能优化算法 / 双自适应扩展卡尔曼滤波算法 / 荷电状态 / 健康状态 / 联合估计

Abstract

收起

The precise identification of model parameters for lithium-ion batteries is crucial for the observing of remaining battery capacity and extending the service life. However, traditional parameter identification methods are often affected by parameter coupling, getting trapped in a local optimum, and other factors, which reduce the estimation accuracy and subsequently lower the accuracy of the subsequent joint estimation. Therefore, an SOC-SOH joint estimation based on a three-stage parameter identification method is proposed. In the first stage, an RC model with fast and slow dynamic modules is constructed based on the internal dynamic characteristics of the battery. In the second stage, the fast dynamic (FD) module uses the forgetting factor recursiveleast squares (FFRLS) adaptive parameter identification method to identify the parameters R₀, R₁, and C₁ with a forgetting factor. In the third stage, the slow dynamic (SD) module uses the adaptive extended Kalman filter algorithm (AEKF) for online parameter identification, and combines the zebra optimization algorithm (ZOA) to solve the local optimum problem and further improve the accuracy of the parameter identification of U_OC, R₂, and C₂. Considering that battery capacity and battery state have different time-varying characteristics, a multi-time scale method is used for the SOC-SOH joint estimation to improve the estimation accuracy. The simulation results show that the improved joint estimation method has better estimation characteristics than traditional methods in terms of average error and maximum error of SOC estimation under different temperatures and operating conditions.

Key words

Zebra optimization algorithm (ZOA) / dual adaptive extended Kalman filter (DAEKF) algorithm / state of charge (SOC) / state of health (SOH) / joint estimation

引用本文

侯健敏, 钱海峰, 李紫祥, 杨文静. 基于三阶段参数辨识的锂离子电池SOC-SOH联合估计. 电源学报, 2026 , 24 (9) : 132 -144 . DOI: 10.13234/j.issn.2095-2805.2026.9.132

Jianmin HOU, Haifeng QIAN, Zixiang LI, Wenjing YANG. Joint SOC-SOH Estimation of Lithium-ion Batteries Based on Three-stage Parameter Identification[J]. Journal of Power Supply, 2026 , 24 (9) : 132 -144 . DOI: 10.13234/j.issn.2095-2805.2026.9.132

基金

收起

国家自然科学基金资助项目(52077105)
江苏省输配电装备技术重点实验室自主科研课题资助项目(2023JSSPD12)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

[1]

Zhang Shuzhi, Guo Xu, Dou Xiaoxin, et al. A data-driven Coulomb counting method for state of charge calibration and estimation of lithium-ion battery[J]. Sustainable Energy Technologies and Assessments, 2020, 40: 100752.

[2]

Mohammadi F. Lithium-ion battery state-of-charge estimation based on an improved Coulomb-counting algorithm and uncertainty evaluation[J]. Journal of Energy Storage, 2022, 48: 104061.

[3]

白俊琦, 贤燕华. 基于BP-多时间尺度UKF优化算法的SOC估计[J]. 电源学报, 2025, 23(3): 363-372. Bai Junqi, Xian Yanhua. State-of-charge estimation based on BP-multi-timescale UKF optimization algorithm[J]. Journal of Power Supply, 2025, 23(3): 363-372. (in Chinese)

[4]

施智鑫, 王育飞, 桑一岩. 基于TCN-GRU-GAT的数字孪生锂离子电池SOH估计方法[J]. 电源技术, 2026, 50(4): 672-680. Shi Zhixin, Wang Yufei, Sang Yiyan. Digital twin lithiumion battery SOH estimation method based on TCN-GRU-GAT[J]. Chinese Journal of Power Sources, 2026, 50(4): 672-680. (in Chinese)

[5]

宋显华, 姚全正. 基于片段充电数据和DEKF-WNN-WLSTM的锂电池健康状态实时估计[J]. 电工技术学报, 2024, 39(5): 1565-1576. Song Xianhua, Yao Quanzheng. Real-time state of health estimation for lithium-ion batteries based on daily segment charging data and dual extended Kalman filters-wavelet neural network-wavelet long short-term memory neural network[J]. Transactions of China Electrotechnical Society, 2024, 39(5): 1565-1576. (in Chinese)

[6]

李凌峰, 宫明辉, 乌江. 采用多模模型的锂离子电池荷电状态联合估计算法[J]. 西安交通大学学报, 2021, 55(1): 78-85. Li Lingfeng, Gong Minghui, Wu Jiang. Joint estimation algorithm for state of charge of Li-ion battery with multi-mode model[J]. Journal of Xi’an Jiaotong University, 2021, 55(1): 78-85. (in Chinese)

[7]

申江卫, 高承志, 舒星, 等. 基于迁移模型的锂离子电池宽温度全寿命SOC与可用容量联合估计[J]. 电工技术学报, 2023, 38(11): 3052-3063. Shen Jiangwei, Gao Chengzhi, Shu Xing, et al. Joint estimation of SOC and usable capacity of lithium-ion battery with wide temperature and full life based on migration model[J]. Transactions of China Electrotechnical Society, 2023, 38(11): 3052-3063. (in Chinese)

[8]

吴宽. 锂离子动力电池建模与状态估计研究[D]. 武汉: 华中科技大学, 2020. Wu Kuan. Research on modeling and state of estimation of lithium-ion power battery[D]. Wuhan: Huazhong University of Science and Technology, 2020. (in Chinese)

[9]

高铭琨, 徐海亮, 吴明铂. 基于等效电路模型的动力电池SOC估计方法综述[J]. 电气工程学报, 2021, 16(1): 90-102. Gao Mingkun, Xu Hailiang, Wu Mingbo. Review of SOC estimation methods for power battery based on equivalent circuit model[J]. Journal of Electrical Engineering, 2021, 16(1): 90-102. (in Chinese)

[10]

贠祥, 张鑫, 王超, 等. 基于联合参数辨识的粒子群优化扩展粒子滤波的锂电池荷电状态估计[J]. 电工技术学报, 2024, 39(2): 595-606. Yun Xiang, Zhang Xin, Wang Chao, et al. State of charge estimation of Li-ion battery using particle swarm optimazation extended Kalman particle filter based on joint parameter identification[J]. Transactions of China Electrotechnical Society, 2024, 39(2): 595-606. (in Chinese)

[11]

Ouyang Tiancheng, Xu Peihang, Chen Jingxian, et al. Improved parameters identification and state of charge estimation for lithium-ion battery with real-time optimal forgetting factor[J]. Electrochimica Acta, 2020, 353: 136576.

[12]

Tang Ruoli, Zhang Shihan, Zhang Shangyu, et al. Model parameter identification for lithium-ion batteries using adaptive multi-context cooperatively co-evolutionary parallel differential evolution algorithm[J]. Journal of Energy Storage, 2023, 58: 106432.

[13]

章军辉, 李庆, 陈大鹏, 等. 基于快速SR-UKF的锂离子动力电池SOC联合估计[J]. 工程科学学报, 2021, 43(7): 976-984. Zhang Junhui, Li Qing, Chen Dapeng, et al. Real-time SOC co-estimation algorithm for Li-ion batteries based on fast square-root unscented Kalman filters[J]. Chinese Journal of Engineering, 2021, 43(7): 976-984. (in Chinese)

[14]

王志福, 罗崴, 闫愿, 等. 基于多方法融合的锂离子电池SOC-SOH联合估计[J]. 北京理工大学学报, 2023, 43(6): 575-584. Wang Zhifu, Luo Wei, Yan Yuan, et al. Joint SOC-SOH estimation for Li-ion batteries based on multi-method fusion[J]. Transactions of Beijing Institute of Technology, 2023, 43(6): 575-584. (in Chinese)

[15]

赵靖英, 胡劲, 张雪辉, 等. 基于锂电池模型和分数阶理论的SOC-SOH联合估计[J]. 电工技术学报, 2023, 38(17): 4551-4563. Zhao Jingying, Hu Jin, Zhang Xuehui, et al. Joint estimation of the SOC-SOH based on lithium battery model and fractional order theory[J]. Transactions of China Electrotechnical Society, 2023, 38(17): 4551-4563. (in Chinese)

[16]

Zhang Cheng, Allafi W, Dinh Q, et al. Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique[J]. Energy, 2018, 142: 678-688.

[17]

Yang Zhao, Wang Xuemei. An improved parameter identification method considering multi-timescale characteristics of lithium-ion batteries[J]. Journal of Energy Storage, 2023, 59: 106462.

[18]

刘晓静, 李建良, 南忠良, 等. 基于改进FFRLS算法的锂离子电池参数辨识[J]. 电源技术, 2022, 46(6): 657-660. Liu Xiaojing, Li Jianliang, Nan Zhongliang, et al. Parameter identification of Li-ion battery based on improved FFRLS algorithm[J]. Chinese Journal of Power Sources, 2022, 46(6): 657-660. (in Chinese)

[19]

Dai Haifeng, Xu Tianjiao, Zhu Letao, et al. Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales[J]. Applied Energy, 2016, 184: 119-131.

[20]

徐帅. 锂离子电池的SOC和SOH估算的复合算法研究[D]. 南京: 南京航空航天大学, 2021. Xu Shuai. Study on composite algorithms of state-of-charge and state-of-health estimation for lithium-ion battery[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2021. (in Chinese)

[21]

鲍明堃, 周扬忠. 基于双模型的递推最小二乘永磁同步直线电机电气参数在线辨识[J]. 电源学报, 2025, 23(3): 343-353. Bao Mingkun, Zhou Yangzhong. Dual-model-based recursive least squares online identification of electrical parameters of permanent magnet synchronous linear motor[J]. Journal of Power Supply, 2025, 23(3): 343-353. (in Chinese)

[22]

赵转, 曹以龙, 杜君莉, 等. 遗忘因子递推最小二乘法辨识锂离子电池参数[J]. 电池, 2023, 53(6): 629-633. Zhao Zhuan, Cao Yilong, Du Junli, et al. Identification of Li-ion battery parameters by forgetting factor recursive least square method[J]. Battery Bimonthly, 2023, 53(6): 629-633. (in Chinese)

[23]

李浩楠, 李其轩, 倪瓒, 等. 基于卡尔曼滤波的太阳跟踪装置控制策略[J]. 实验室研究与探索, 2022, 41(10): 39-44, 64. Li Haonan, Li Qixuan, Ni Zan, et al. Research on control strategy of sun tracking device based on Kalman filter[J]. Research and Exploration in Laboratory, 2022, 41(10): 39-44, 64. (in Chinese)

[24]

李泓沛, 刘桂雄, 邓威. 动态工况模拟下动力锂电池建模与参数辨识方法[J]. 电子测量技术, 2022, 45(14): 103-108. Li Hongpei, Liu Guixiong, Deng Wei. Modeling and parameter identification method for power lithium batteries under dynamic working condition simulation[J]. Electronic Measurement Technology, 2022, 45(14): 103-108. (in Chinese)

[25]

TrojovskáE, Dehghani M, Trojovský P. Zebra optimization algorithm: A new bio-inspired optimization algorithm for solving optimization algorithm[J]. IEEE Access, 2022, 10: 49445-49473.

[26]

印学浩, 宋宇晨, 刘旺, 等. 基于多时间尺度的锂离子电池状态联合估计[J]. 仪器仪表学报, 2018, 39(8): 118-126. Yin Xuehao, Song Yuchen, Liu Wang, et al. Multi-scale state joint estimation for lithium-ion battery[J]. Chinese Journal of Scientific Instrument, 2018, 39(8): 118-126. (in Chinese)

[27]

海涛, 范恒, 陆代强, 等. 基于IMDEKF的SoC-SoH联合估计[J]. 实验室研究与探索, 2021, 40(7): 111-115, 139. Hai Tao, Fan Heng, Lu Daiqiang, et al. SoC-SoH joint estimation based on IMDEKF[J]. Research and Exploration in Laboratory, 2021, 40(7): 111-115, 139. (in Chinese)

[28]

陈秉琳, 赵景全. 锂离子电池循环寿命影响因素及机理研究进展[J]. 电源技术, 2025, 49(1): 57-66. Chen Binglin, Zhao Jingquan. Research progress on influencing factors and mechanisms of cycle life of lithium-ion batteries[J]. Chinese Journal of Power Sources, 2025, 49(1): 57-66. (in Chinese)

[29]

罗玉涛, 吴志强. 基于DAEKF算法的锂离子电池主要状态在线联合估计[J]. 华南理工大学学报(自然科学版), 2023, 51(1): 84-94. Luo Yutao, Wu Zhiqiang. Online joint estimation of main states of lithium-ion battery based on DAEKF algorithm[J]. Journal of South China University of Technology (Natural Science Edition), 2023, 51(1): 84-94. (in Chinese)

[30]

Xiong Rui, Sun Fengchun, Chen Zheng, et al. A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles[J]. Applied Energy, 2014, 113: 463-476.

2026年第24卷第9期

PDF下载

引用本文

BibTeX

文章信息

doi: 10.13234/j.issn.2095-2805.2026.9.132

接收时间：2025-01-04
首发时间：2026-10-29
出版时间：2026-09-30

补充材料

相关文章

文章信息

作者

出版历史

收稿日期：2025-01-04
修回日期：2025-02-19
录用日期：2025-02-28

基金

National Natural Science Foundation of China(52077105)

国家自然科学基金资助项目(52077105)

Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology(2023JSSPD12)

江苏省输配电装备技术重点实验室自主科研课题资助项目(2023JSSPD12)

作者信息

^1.南京信息工程大学自动化学院，南京　210044

^2.江苏省输配电装备技术重点实验室，常州　213022

通讯作者:

钱海峰（2000—　），男，通信作者，硕士研究生。研究方向：电动汽车充电安全。E-mail：2659353390@qq.com。

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.9.132

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

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

关闭全屏

BibTeX
EndNote
RefWorks
TxT