电源学报

基于粒子和容积卡尔曼滤波的锂离子电池SOC估计

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彭云海 , 张尧中国电源学会会员 , 张帆 , 于龙杰

电源学报 | 新能源发电与储能 2026,24(5): 159-167

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电源学报 |新能源发电与储能 2026 , 24 (5) : 159 -167

基于粒子和容积卡尔曼滤波的锂离子电池SOC估计

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彭云海（2000—　），男，硕士研究生。研究方向：大规模储能技术。E-mail：221060012@hdu.edu.cn。

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张帆（1976—　），男，博士，教授级高级工程师。研究方向：电动车与储能。E-mail：zhangfan@hdu.edu.cn。

于龙杰（1999—　），男，硕士研究生。研究方向：电池均衡技术。E-mail：ljyu1337@163.com。

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于龙杰（1999—　），男，硕士研究生。研究方向：电池均衡技术。E-mail：ljyu1337@163.com。

彭云海, 张尧中国电源学会会员, 张帆, 于龙杰

作者信息

杭州电子科技大学自动化学院，杭州　310016

通讯作者:

张尧（1979—　），男，中国电源学会会员，通信作者，博士，副教授。研究方向：大规模储能技术。E-mail：yaozhang@hdu.edu.cn。

作者简介:

彭云海（2000—　），男，硕士研究生。研究方向：大规模储能技术。E-mail：221060012@hdu.edu.cn。

张帆（1976—　），男，博士，教授级高级工程师。研究方向：电动车与储能。E-mail：zhangfan@hdu.edu.cn。

于龙杰（1999—　），男，硕士研究生。研究方向：电池均衡技术。E-mail：ljyu1337@163.com。

SOC Estimation of Lithium-ion Batteries Based on Particle and Cubature Kalman Filters

Yunhai PENG, Yao ZHANG Member, CPSS, Fan ZHANG, Longjie YU

Affiliations

School of Automation, Hangzhou Dianzi University, Hangzhou 310016, China

出版时间: 2026-05-30 doi: 10.13234/j.issn.2095-2805.2026.5.159

文章导航

摘要

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锂离子电池荷电状态SOC（state of charge）的准确估计对电池管理系统实现电池均衡充放电、延长电池使用寿命有着重要意义。针对锂离子电池复杂的化学特性和卡尔曼滤波算法估计SOC的非时变特性，提出一种新型锂离子电池SOC估计方法。首先，结合锂离子电池模型的精度和实用性，提出锂离子电池二阶等效模型；然后利用自适应遗忘因子的递推最小二乘法AFFRLS（adaptive forgetting factor recursive least squares）对二阶模型的阻容参数进行在线辨识，建立阻容参数滤波关系式，提出改进粒子滤波PF（particle filter）和容积卡尔曼滤波CKF（cubature Kalman filter）算法结合的粒子容积卡尔曼滤波PF-CKF算法估计锂离子电池SOC。最后，设计混合脉冲特性HPPC（mixed power pulse characteristics）实验验证该方法的准确性和稳定性。

关键词

锂离子电池 / 荷电状态估计 / 自适应遗忘因子 / 改进粒子滤波 / 容积卡尔曼滤波

Abstract

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Accurate estimation of State of Charge (SOC) for lithium-ion batteries is crucial for implementing battery balancing during charging and discharging, thus extending battery lifespan. Given the complex chemical characteristics of lithium-ion batteries and the non-time-varying nature of SOC estimation using the Kalman filter algorithm, a novel method for SOC estimation for lithium-ion batteries is proposed. Firstly, combining the precision and practicality of lithium-ion battery models, a second-order equivalent model for lithium-ion batteries is presented. Subsequently, utilizing the Adaptive Forgetting Factor Recursive Least Squares (AFFRLS) algorithm with adaptive forgetting factors, the impedance-capacitance parameters of the second-order model are identified online, establishing a filtering relationship for impedance-capacitance parameters. The proposed method integrates Particle Filter (PF) and Cubature Kalman Filter (CKF) algorithms, termed Particle Cubature Kalman Filter (PF-CKF), to estimate SOC for lithium-ion batteries. Finally, Hybrid Pulse Power Characterization (HPPC) experiments are designed to validate the accuracy and stability of this method.

Key words

Lithium-ion battery / state of charge / adaptive forgetting factor / improved particle filter / cubature Kalman filter

引用本文

彭云海, 张尧中国电源学会会员, 张帆, 于龙杰. 基于粒子和容积卡尔曼滤波的锂离子电池SOC估计. 电源学报, 2026 , 24 (5) : 159 -167 . DOI: 10.13234/j.issn.2095-2805.2026.5.159

Yunhai PENG, Yao ZHANG, Fan ZHANG, Longjie YU. SOC Estimation of Lithium-ion Batteries Based on Particle and Cubature Kalman Filters[J]. Journal of Power Supply, 2026 , 24 (5) : 159 -167 . DOI: 10.13234/j.issn.2095-2805.2026.5.159

基金

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国家自然科学基金面上项目(51877058)
浙江省“尖兵”研发攻关计划资助项目(2024C01018)

参考文献

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2026年第24卷第5期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.5.159

接收时间：2024-04-01
首发时间：2026-10-29
出版时间：2026-05-30

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出版历史

收稿日期：2024-04-01
修回日期：2024-05-14
录用日期：2024-05-29

基金

General Progran of National Natural Science Foundation of China(51877058)

国家自然科学基金面上项目(51877058)

Zhejiang Province Pioneer Plan Project(2024C01018)

浙江省“尖兵”研发攻关计划资助项目(2024C01018)

作者信息

杭州电子科技大学自动化学院，杭州　310016

通讯作者:

张尧（1979—　），男，中国电源学会会员，通信作者，博士，副教授。研究方向：大规模储能技术。E-mail：yaozhang@hdu.edu.cn。

参考文献

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.5.159

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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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