电源学报

基于自适应平方根秩滤波算法的锂电池SOC估计

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张传明 ¹^,² , 万佑红 ¹^,² , 肖杨 ¹^,² , 陈子龙 ¹^,²

电源学报 | 新能源发电与储能 2026,24(3): 153-160

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电源学报 |新能源发电与储能 2026 , 24 (3) : 153 -160

基于自适应平方根秩滤波算法的锂电池SOC估计

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张传明¹^,², 万佑红¹^,², 肖杨¹^,², 陈子龙¹^,²

作者信息

^1.南京邮电大学自动化学院，南京　210023

^2.南京邮电大学人工智能学院，南京　210023

通讯作者:

万佑红（1974—　），女，通信作者，博士，教授。研究方向：锂电池SOC估计与储能关键技术，复杂网络的建模与优化及其在电力系统中的应用。E-mail：wanyh@njupt.edu.cn。

作者简介:

张传明（1998—　），男，硕士研究生。研究方向：锂电池SOC估计。E-mail：zhangcm0203@163.com。

肖杨（1999—　），男，硕士研究生。研究方向：储能电站综合评价。E-mail：2227624023@qq.com。

陈子龙（1999—　），男，硕士研究生。研究方向：锂电池SOC估计。E-mail：532379660@qq.com。

SOC Estimation of Lithium Battery Based on Adaptive Square Root Rank Filter Algorithm

Chuanming ZHANG¹^,², Youhong WAN¹^,², Yang XIAO¹^,², Zilong CHEN¹^,²

Affiliations

^1.College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

^2.College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

出版时间: 2026-03-30 doi: 10.13234/j.issn.2095-2805.2026.3.153

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摘要

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锂电池荷电状态SOC（state-of-charge）的估计性能对电池管理系统起着至关重要的作用。采用带遗忘因子的递推最小二乘法辨识电池模型参数，针对现有SOC估计算法中滤波器的采样不能很好地模拟系统状态概率分布的问题，引入秩采样方法，提出自适应平方根秩滤波算法，以平方根形式对系统状态的协方差矩阵进行迭代更新，且每次迭代依据新息自适应地调整观测噪声，以提高SOC的估计精度及其对噪声的自适应能力。通过在联邦城市驾驶FUDS（federal urban driving schedule）和动态压力测试DST（dynamic stress test）工况下的仿真实验来验证所提算法的性能，结果表明，所提算法与扩展卡尔曼滤波、无迹卡尔曼滤波及秩滤波相比，在收敛速度和估计精度方面均有所提升。

关键词

锂电池 / 荷电状态 / 递推最小二乘法 / 自适应 / 平方根 / 秩滤波

Abstract

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The estimation performance of state-of-charge (SOC) of lithium battery plays a vital role in abattery management system. The recursive least squares method with forgetting factor was used to identify the parameters of a battery model. Aimed at the problem that the sampling of a filter in the existing SOC estimation algorithm cannot well simulate the probability distribution of the system state, a rank sampling method was introduced, and an adaptive square root rank filter algorithm was proposed. The covariance matrix of the system state was updated iteratively in the form of square root, and the observation noise was adjusted adaptively according to the innovation in each iteration, so as to improve the SOC estimation accuracy and its adaptive capability with respect to noise. The performance of the proposed algorithm was verified by simulation experiments under FUDS and DST conditions. Results show that the proposed algorithm can improve the convergence speed and estimation accuracy compared with extended Kalman filter, unscented Kalman filter and rank filter.

Key words

Lithium battery / state-of-charge (SOC) / recursive least squares method / adaptive / square root / rank filter

引用本文

张传明, 万佑红, 肖杨, 陈子龙. 基于自适应平方根秩滤波算法的锂电池SOC估计. 电源学报, 2026 , 24 (3) : 153 -160 . DOI: 10.13234/j.issn.2095-2805.2026.3.153

Chuanming ZHANG, Youhong WAN, Yang XIAO, Zilong CHEN. SOC Estimation of Lithium Battery Based on Adaptive Square Root Rank Filter Algorithm[J]. Journal of Power Supply, 2026 , 24 (3) : 153 -160 . DOI: 10.13234/j.issn.2095-2805.2026.3.153

基金

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国家自然科学基金资助项目(62073172)

参考文献

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参考文献引证文献

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2026年第24卷第3期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.3.153

接收时间：2023-03-09
首发时间：2026-10-29
出版时间：2026-03-30

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出版历史

收稿日期：2023-03-09
修回日期：2023-07-17
录用日期：2023-12-02

基金

National Natural Science Foundation of China(62073172)

国家自然科学基金资助项目(62073172)

作者信息

^1.南京邮电大学自动化学院，南京　210023

^2.南京邮电大学人工智能学院，南京　210023

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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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