基于退化注入场路耦合模型的锂电池SOC估计方法

doi:10.12305/j.issn.1001-506X.2025.10.14

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (10): 3270-3277.doi: 10.12305/j.issn.1001-506X.2025.10.14

• 系统工程 • 上一篇

基于退化注入场路耦合模型的锂电池SOC估计方法

孙博¹^,², 林桐疏¹, 吴泽豫¹^,²^,*, 钱诚¹^,², 潘俊林¹, 周乐阳¹, 董珂鸣³

1. 北京航空航天大学可靠性与系统工程学院，北京 100191
2. 可靠性与环境工程技术国家级重点实验室，北京 100191
3. 航天科工防御技术研究试验中心，北京 100854

收稿日期:2024-09-23 出版日期:2025-10-25 发布日期:2025-10-23
通讯作者: 吴泽豫
作者简介:孙　博（1979—），男，研究员，博士，主要研究方向为可靠性综合集成、可靠性数字孪生
林桐疏（2001—），男，硕士研究生，主要研究方向为可靠性仿真、健康状态预测
钱　诚（1983—），男，副研究员，博士，主要研究方向为锂电池系统使役可靠性
潘俊林（1996—），男，博士研究生，主要研究方向为数据驱动、健康状态预测
周乐阳（2000—），男，博士研究生，主要研究方向为可靠性数字孪生、健康状态预测
董珂鸣（1991—），男，工程师，主要研究方向为质量保证

SOC estimation method for lithium-ion batteries based on degradation injected field-circuit coupling model

Bo SUN¹^,², Tongshu LIN¹, Zeyu WU¹^,²^,*, Cheng QIAN¹^,², Junlin PAN¹, Leyang ZHOU¹, Keming DONG³

1. School of Reliability and Systems Engineering，Beihang University，Beijing 100191，China
2. National Key Laboratory of Reliability and Environmental Engineering Technology，Beijing 100191，China
3. Aerospace Science and Industry Defense Technology Research Testing Center，Beijing 100854，China

Received:2024-09-23 Online:2025-10-25 Published:2025-10-23
Contact: Zeyu WU

摘要/Abstract

摘要：

针对目前荷电状态（state of charge, SOC）估计方法考虑温度与退化共同影响及其关联耦合关系较少，导致电池性能退化后的模型表征不完善、SOC估计精度不足的问题，提出一种基于退化注入场路耦合模型的锂电池SOC估计方法，以实现全寿命周期下SOC的准确估计。首先建立等效电路模型与多物理场模型耦合的场路耦合模型，刻画温度的影响；进而使用离线参数辨识方法将温度、退化等因素注入等效电路模型参数中；最终建立代理模型提高计算效率，实现在线SOC估计。案例验证结果表明，在锂电池经过长时间运行发生退化后，相比于其他方法，所提方法的估计结果具有更平稳的曲线和更高的精度。

关键词: 锂电池, 荷电状态估计, 场路耦合模型, 退化注入, 代理模型

Abstract:

At present, the estimation methods for state of charge (SOC) takes little account of the combined effects of temperature and degradation, as well as their coupling relationship, which leads to incomplete model representation and insufficient SOC estimation accuracy after battery performance degradation. In regard to this, a lithium-ion battery SOC estimation method based on degradation injection field-circuit coupling model is proposed to achieve accurate estimation of SOC throughout the entire lifecycle. Firstly, a field-circuit coupled model by coupling the equivalent circuit model with the multi physics field model is established to describe the effect of temperature. Then, the offline parameter identification method is used to inject factors such as temperature and degradation into the parameters of the equivalent circuit model. Finally, a surrogate model is built to improve computational efficiency and achieve online SOC estimation. The verification results of the case indicate that compared to other methods, the proposed model has a smoother prediction curve and higher accuracy in estimating the degradation of lithium-ion batteries after long-term operation.

Key words: lithium-ion battery, state of charge （SOC） estimation, field-circuit coupling model, degradation injection, surrogate model

中图分类号:

TM 912

孙博, 林桐疏, 吴泽豫, 钱诚, 潘俊林, 周乐阳, 董珂鸣. 基于退化注入场路耦合模型的锂电池SOC估计方法[J]. 系统工程与电子技术, 2025, 47(10): 3270-3277.

Bo SUN, Tongshu LIN, Zeyu WU, Cheng QIAN, Junlin PAN, Leyang ZHOU, Keming DONG. SOC estimation method for lithium-ion batteries based on degradation injected field-circuit coupling model[J]. Systems Engineering and Electronics, 2025, 47(10): 3270-3277.

图/表 14

图1

图2

图3

图4

图5

表1

表2

图6

图7

图8

图9

图10

图11

表3

参考文献 31

1	WANG Y J, ZHANG X C, LI K Q, et al. Perspectives and challenges for future lithium-ion battery control and management[J]. eTransportation, 2023, 18, 100260. doi: 10.1016/j.etran.2023.100260
2	RUAN H K, HE H W, QUAN Z Y, et al. State of health estimation of lithium-ion battery based on constant-voltage charging reconstruction[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2023, 11 (4): 4393- 4402. doi: 10.1109/JESTPE.2021.3098836
3	YU Z H, TIAN Y, LI B H. A simulation study of Li-ion batteries based on a modified P2D model[J]. Journal of Power Sources, 2024, 618, 234376. doi: 10.1016/j.jpowsour.2024.234376
4	LI J, ADEWUYI K, LOTFI N, et al. A single particle model with chemical/mechanical degradation physics for lithium ion battery state of health （SOH） estimation[J]. Applied Energy, 2018, 212, 1178- 1190. doi: 10.1016/j.apenergy.2018.01.011
5	WANG Z, SHEN J R, XU Y. State-of-charge estimation for lithium-ion battery base on adaptive extended sliding innovation filter[J]. Energies, 2024, 17 (14): 3495. doi: 10.3390/en17143495
6	ZHOU H, WANG S L, YU C M, et al. Research on SOC estimation for lithium ion batteries based on improved PNGV equivalence model and AF-UKF algorithm[J]. International Journal of Electrochemical Science, 2022, 17 (8): 220836. doi: 10.20964/2022.08.31
7	LI Y G, QI H Z, SHI X L, et al. A physics-based equivalent circuit model and state of charge estimation for lithium-ion batteries[J]. Energies, 2024, 17 (15): 3782. doi: 10.3390/en17153782
8	TIAN E G, CHEN H, WANG C S, et al. Security-ensured state of charge estimation of lithium-ion batteries subject to malicious attacks[J]. IEEE Trans. on Smart Grid, 2023, 14 (3): 2250- 2261. doi: 10.1109/TSG.2022.3202811
9	HOW D N T, HANNAN M A, HOSSAIN LIPU M S, et al. State of charge estimation for lithium-ion batteries using model-based and data-driven methods: a review[J]. IEEE Access, 2019, 7, 136116- 136136. doi: 10.1109/ACCESS.2019.2942213
10	WANG M D, JIA P, WEI W Q, et al. Electro-thermal coupling modeling of energy storage station considering battery physical characteristics[J]. Frontiers in Energy Research, 2024, 12, 1433797. doi: 10.3389/fenrg.2024.1433797
11	TABINE A, LAADISSI E M, ELACHHAB A, et al. Innovative method to precise SOC estimation for lithium-ion batteries under diverse temperature and current conditions[J]. International Journal of Hydrogen Energy, 2024, 96, 1299- 1309. doi: 10.1016/j.ijhydene.2024.11.385
12	蔡忠义, 王泽洲, 张晓丰, 等. 隐含非线性退化设备的剩余寿命在线预测方法[J]. 系统工程与电子技术, 2020, 42 (6): 1410- 1416. doi: 10.3969/j.issn.1001-506X.2020.06.27
	CAI Z Y, WANG Z Z, ZHANG X F, et al. Online prediction method of remaining useful lifetime for implicit nonlinear degradation equipment[J]. Systems Engineering and Electronics, 2020, 42 (6): 1410- 1416. doi: 10.3969/j.issn.1001-506X.2020.06.27
13	YU H Q, CAO M D, LIU Y J, et al. A fusion method for capacity estimation of fast-charging lithium-ion battery under varied degradation mode[J]. Journal of Energy Storage, 2023, 73, 109145. doi: 10.1016/j.est.2023.109145
14	LYU D Z, ZHANG B, LIU E H, et al. Prognosis-enabled battery SOC estimation using a closed-loop approach with consideration of SOH degradation[J]. Journal of Energy Storage, 2025, 106, 113713. doi: 10.1016/j.est.2024.113713
15	HUANG K, LV Z T, YAO K X, et al. Co-estimation of maximum available capacity and state-of-charge for lithium-ion batteries in multi-operating mode with temperature and degradation state adaptivity[J]. Measurement, 2024, 225, 114019. doi: 10.1016/j.measurement.2023.114019
16	朱卫平, 陈国旺, 卫志农, 等. 基于VFFRLS算法的锂电池参数辨识[J]. 电力工程技术, 2023, 42 (1): 226- 233. doi: 10.12158/j.2096-3203.2023.01.027
	ZHU W P, CHEN G W, WEI Z N, et al. Parameter identification of lithium-ion battery based on least squares algonithm with variable forgetting factor[J]. Electric Power Engineering Technology, 2023, 42 (1): 226- 233. doi: 10.12158/j.2096-3203.2023.01.027
17	LI J L, PENG Y C, WANG Q, et al. Status and prospects of research on lithium-ion battery parameter identification[J]. Batteries, 2024, 10 (6): 194. doi: 10.3390/batteries10060194
18	SHRIVASTAVA P, SOON T K, IDRIS M Y I B, et al. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries[J]. Renewable and Sustainable Energy Reviews, 2019, 113, 109233. doi: 10.1016/j.rser.2019.06.040
19	潘海鸿, 张沫, 王惠民, 等. 基于多影响因素建立锂离子电池充电内阻的动态模型[J]. 电工技术学报, 2021, 36 (10): 2199- 2206.
	PAN H H, ZHANG M, WANG H M, et al. Establishing a dynamic model of lithium-ion battery charging internal resistance based on multiple factors[J]. Transactions of China Electrotechnical Society, 2021, 36 (10): 2199- 2206.
20	AN Q, PENG J. Parameter identification of lithium battery pack based on novel cooperatively coevolving differential evolution algorithm[J]. Renewable Energy, 2023, 216, 119036. doi: 10.1016/j.renene.2023.119036
21	LUCAFERRI V, QUERCIO M, LAUDANI A, et al. A review on battery model-based and data-driven methods for battery management systems[J]. Energies, 2023, 16 (23): 7807. doi: 10.3390/en16237807
22	ZHANG R F, XIA B Z, LI B H, et al. State of the art of lithium-ion battery SOC estimation for electrical vehicles[J]. Energies, 2018, 11 (7): 1820. doi: 10.3390/en11071820
23	WANG L B, LI J P, CHEN J Y, et al. Revealing the internal short circuit mechanisms in lithium-ion batteries upon dynamic loading based on multiphysics simulation[J]. Applied Energy, 2023, 351, 121790. doi: 10.1016/j.apenergy.2023.121790
24	LIU J, YADAV S, SALMAN M, et al. Review of thermal coupled battery models and parameter identification for lithium-ion battery heat generation in EV battery thermal management system[J]. International Journal of Heat and Mass Transfer, 2024, 218, 124748. doi: 10.1016/j.ijheatmasstransfer.2023.124748
25	BERNARDI D, PAWLIKOWSKI E, NEWMAN J. A general energy- balance for battery systems[J]. Journal of the Electrochemical Society, 1985, 132 (1): 5- 12. doi: 10.1149/1.2113792
26	方安然, 李旦, 张建秋. 异常值和未知观测噪声鲁棒的卡尔曼滤波器[J]. 系统工程与电子技术, 2021, 43 (3): 593- 602. doi: 10.12305/j.issn.1001-506X.2021.03.01
	FANG A R, LI D, ZHANG J Q. Outlier and unknown observation noise robust Kalman filter[J]. Systems Engineering and Electronics, 2021, 43 (3): 593- 602. doi: 10.12305/j.issn.1001-506X.2021.03.01
27	MONIRUL I M, QIU L, RUBY R, et al. Accurate SOC estimation in power lithium-ion batteries using adaptive extended Kalman filter with a high-order electrical equivalent circuit model[J]. Measurement, 2025, 249, 117081. doi: 10.1016/j.measurement.2025.117081
28	陈大豪, 程志君, 钟健, 等. 定量和定性因子混合的罚盲克里金模型[J]. 系统工程与电子技术, 2025, 47 (1): 153- 163.
	CHEN D H, CHENG Z J, ZHONG J, et al. Penalty blind Kriging model with quantitative and qualitative factors[J]. Systems Engineering and Electronics, 2025, 47 (1): 153- 163.
29	BOLE B, KULKARNI C S, DAIGLE M. Adaptation of an electrochemistry-based li-ion battery model to account for deterioration observed under randomized use[C]//Proc. of the Annual Conference of the Prognostics and Health Management Society, 2014.
30	李堂, 黄康, 毛行奎, 等. 基于EKF算法的动力锂离子电池SOC估计[J]. 电器与能效管理技术, 2023, (9): 62- 68，75.
	LI T, HUANG K, MAO X K, et al. SOC estimation of power lithium-ion battery based on EKF algorithm[J]. Electrical & Energy Management Technology, 2023, (9): 62- 68，75.
31	CHEN G X, PENG W W, YANG F F. An LSTM-SA model for SOC estimation of lithium-ion batteries under various temperatures and aging levels[J]. Journal of Energy Storage, 2024, 84, 110906. doi: 10.1016/j.est.2024.110906

工况名称	工况介绍
随机充/放电	从−4.5 A，−3.75 A，−3 A，−2.25 A，−1.5 A，−0.75 A，0.75 A，1.5 A，2.25 A，3 A，3.75 A，4.5 A中随机选择充电或放电电流，直到电池电压达到3.2 V或4.2 V，或充放电持续5 min
随机充/放电后休息	小于1 s，用于选择新的充电或放电电流大小
参考充/放电	用于获取每1500随机充放电循环后的电池容量，首先以恒流-恒压方式充满电池，随后以2 A放电，直到电池电压达到3.2 V
脉冲充/放电	用于获取每3000随机充放电循环后的电池参数，首先施加10 min的1 A充/放电电流，随后休息20 min，持续至满充/满放
脉冲充/放电后休息	休息20 min，用于观察脉冲结束后电压的回弹情况
低电流放电	使用0.04 A的低电流放电，用于获取电池开路电压与SOC的关系

参数属性	参数名称	取值
几何参数	电池高度/mm	65
几何参数	电池半径/mm	9
材料参数	导热系数/（$ {\text{W}} \cdot {{\text{m}}^{ - 1}}{{\text{K}}^{ - 1}} $）	$\{ 3, 3, 30\}$
	恒压热容/（$ {\text{J}} \cdot {\text{k}}{{\text{g}}^{ - 1}}{{\text{K}}^{ - 1}} $）	$ 900 $
	密度/（$ {\text{kg}} \cdot {{\text{m}}^{{-\text{ 3}}}} $）	$ 2\;663 $
边界条件	入口速度/（$ {\text{m}} \cdot {{\text{s}}^{{ -\text{ 1}}}} $）	0.5
	出口压强/atm	1
	环境温度/°C	25

方法	退化前		退化后
方法	MAE	RMSE	MAE	RMSE
无退化EKF方法	0.009	0.010	0.201	0.213
仅退化注入的方法	0.009	0.010	0.015	0.021
退化注入+场路耦合的方法	0.007	0.009	0.011	0.014
LSTM方法	0.018	0.022	0.032	0.040

[1]	马力, 师鹏, 陈宇, 李文龙. 天基信息支援体系离散事件仿真与效能评估[J]. 系统工程与电子技术, 2024, 46(3): 906-913.
[2]	刘佳, 杨克巍, 姜江, 夏博远. 基于多保真度代理模型的装备系统参数估计方法[J]. 系统工程与电子技术, 2021, 43(1): 130-137.
[3]	李响, 王晓鹏. 径向基代理模型中形状参数对精度的影响[J]. 系统工程与电子技术, 2015, 37(3): 688-692.
[4]	李君桥，汤俊，孙培林，许建峰. 基于随机状态图的复合大系统仿真方法[J]. 系统工程与电子技术, 2013, 35(10): 2208-2213.

基于退化注入场路耦合模型的锂电池SOC估计方法

SOC estimation method for lithium-ion batteries based on degradation injected field-circuit coupling model

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 31

相关文章 4

编辑推荐

Metrics

本文评价