电源学报

基于不同温度下SOC-OCV曲线重构的锂离子电池SOC估计

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陈贵升 ¹ , 许杨松 ¹ , 李俊达 ² , 沈颖刚 ¹ , 肖仁鑫 ¹ , 杨杰 ¹

电源学报 | 新能源发电与储能 2026,24(2): 116-126

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电源学报 |新能源发电与储能 2026 , 24 (2) : 116 -126

基于不同温度下SOC-OCV曲线重构的锂离子电池SOC估计

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陈贵升¹, 许杨松¹, 李俊达², 沈颖刚¹, 肖仁鑫¹, 杨杰¹

作者信息

^1.昆明理工大学云南省内燃机重点实验室，昆明　650500

^2.云南电网能源投资有限责任公司，昆明　650500

通讯作者:

肖仁鑫（1978—　），男，通信作者，博士，副教授。研究方向：电动汽车动力系统建模、控制与优化。E-mail：1349806835@qq.com。

作者简介:

陈贵升（1979—　），男，博士，教授。研究方向：车辆/航空动力系统高效利用技术开发。E-mail：cgs_yly@163.com。

许杨松（1992—　），男，硕士。研究方向：锂离子电池建模及其荷电状态估计。E-mail：a15087311194@126.com。

李俊达（1988—　），男，硕士，工程师。研究方向：新能源汽车储能，车桩网电互动优化及虚拟电厂运行优化。E-mail：173905000@qq.com。

沈颖刚（1965—　），男，博士，教授。研究方向：车辆动力系统高效利用技术开发。E-mail：shenyinggang@163.com。

杨杰（1991—　），女，硕士，讲师。研究方向：新能源动力系统建模。E-mail：328516727@qq.com。

SOC Estimation of Lithium-ion Batteries Based on Reconstructed SOC-OCV Curves at Different Temperatures

Guisheng CHEN¹, Yangsong XU¹, Junda LI², Yinggang SHEN¹, Renxin XIAO¹, Jie YANG¹

Affiliations

^1.Yunnan Key Laboratory of Internal Combustion Engine, Kunming University of Science and Technology, Kunming 650500, China

^2.Yunnan Power Grid Energy Investment Co., Ltd., Kunming 650500, China

出版时间: 2026-02-28 doi: 10.13234/j.issn.2095-2805.2026.2.116

文章导航

摘要

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锂离子电池荷电状态SOC（state-of-charge）与开路电压OCV（open-circuit voltage）的关系曲线是影响其SOC估计精度的核心因素，传统方法通过增量OCV实验获取SOC-OCV曲线，需要耗费大量的时间。因此，提出1种不同温度下重构SOC-OCV曲线的方法，在提高SOC估算精度的同时缩短实验时间。首先，在室温下通过传统增量OCV实验获取的SOC-OCV曲线确定少量关键OCV测试点。然后，在其他温度下采用粒子群优化算法，以关键少数OCV测试点为边界，以5%SOC为间距辨识非测试区域OCV值，基于OCV测试值和辨识值重构不同温度下的SOC-OCV曲线，该方法在减少OCV测试点的情况下提高了SOC-OCV曲线精度。最后，基于重构SOC-OCV曲线，采用自适应扩展卡尔曼滤波估计SOC。结果表明，不同温度下的SOC估计均方根误差降低40%以上。

关键词

锂离子电池 / 荷电状态估计 / 荷电状态-开路电压曲线重构 / 不同温度 / 自适应扩展卡尔曼滤波

Abstract

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The curve of relationship between the state-of-charge (SOC) and open-circuit voltage (OCV) of lithium-ion batteries is the core factor affecting the SOC estimation accuracy. However, the traditional methods obtain the SOC-OCV curve through incremental OCV tests, which require a lot of time. Under this background, a method for reconstructing SOC-OCV curves at different temperatures is proposed in this paper, so as to improve the SOC estimation accuracy while reducing the experimental time. First, a small number of key OCV test points are determined by the SOC-OCV curve obtained from the traditional incremental OCV test at room temperature. At other temperatures, the particle swarm optimization algorithm is used to identify the OCV value in untested areas, with the determined key OCV test points as the boundary and 5% SOC as an interval. Based on the test and identified values of OCV, the SOC-OCV curves at different temperatures are reconstructed. This method improves the accuracy of SOC-OCV curves while reducing the OCV test points. Based on reconstructed SOC-OCV curves, the SOC is estimated by an adaptive extended Kalman filter. Results show that the root mean square error of SOC estimation decreased by at least 40% at different temperatures.

Key words

Lithium-ion battery / state-of-charge (SOC) estimation / reconstruction of state-of-charge-open-circuit voltage (SOC-OCV) curve / different temperatures / adaptive extended Kalman filter

引用本文

陈贵升, 许杨松, 李俊达, 沈颖刚, 肖仁鑫, 杨杰. 基于不同温度下SOC-OCV曲线重构的锂离子电池SOC估计. 电源学报, 2026 , 24 (2) : 116 -126 . DOI: 10.13234/j.issn.2095-2805.2026.2.116

Guisheng CHEN, Yangsong XU, Junda LI, Yinggang SHEN, Renxin XIAO, Jie YANG. SOC Estimation of Lithium-ion Batteries Based on Reconstructed SOC-OCV Curves at Different Temperatures[J]. Journal of Power Supply, 2026 , 24 (2) : 116 -126 . DOI: 10.13234/j.issn.2095-2805.2026.2.116

基金

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国家自然科学基金资助项目(51567012)

参考文献

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2026年第24卷第2期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.2.116

接收时间：2023-07-29
首发时间：2026-10-29
出版时间：2026-02-28

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出版历史

收稿日期：2023-07-29
修回日期：2023-10-19
录用日期：2023-11-01

基金

National Natural Science Foundation of China(51567012)

国家自然科学基金资助项目(51567012)

作者信息

^1.昆明理工大学云南省内燃机重点实验室，昆明　650500

^2.云南电网能源投资有限责任公司，昆明　650500

通讯作者:

肖仁鑫（1978—　），男，通信作者，博士，副教授。研究方向：电动汽车动力系统建模、控制与优化。E-mail：1349806835@qq.com。

参考文献

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.2.116

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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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