基于CSO-AUKF的锂电池SOC估算方法

doi:10.3969/j.issn.1674-0696.2024.09.15

重庆交通大学学报（自然科学版） ›› 2024, Vol. 43 ›› Issue (9): 118-126.DOI: 10.3969/j.issn.1674-0696.2024.09.15

• 交通装备 • 上一篇

基于CSO-AUKF的锂电池SOC估算方法

吴华伟1, 2，洪强1, 2，陈运星1, 2，马毓博1, 2

(1. 湖北文理学院湖北隆中实验室，湖北襄阳 441053； 2. 湖北文理学院纯电动汽车动力系统设计与测试湖北省重点实验室，湖北襄阳 441053)

收稿日期:2023-10-10 修回日期:2024-04-26 发布日期:2024-09-25
作者简介:吴华伟（1979—），男，湖北襄阳人，教授，博士，主要从事车辆动力学与协同控制、地面载运设备服役状态智能监测及预警、新型载运工具动力系统设计及控制方面的研究。E-mail：whw_xy@hbuas.edu.cn 通信作者：洪强（1999—），男，江苏盐城人，硕士研究生，主要从事机电系统监测和优化方面的研究。E-mail：2315729587@qq.com
基金资助:
襄阳市科技计划湖北隆中实验室专项项目(2023AAA001)；“新能汽车与智慧交通”湖北省优势特色学科群开放基金项目(XKQ2022008)

Lithium Battery SOC Estimation Method Based on CSO-AUKF

WU Huawei1,2, HONG Qiang1,2, CHEN Yunxing1,2, MA Yubo1,2

(1. Hubei Longzhong Laboratory, Hubei University of Arts and Sciences, Xiangyang 441053, Hubei, China; 2. Hubei Key Laboratory of Pure Electric Vehicle Power System Design and Testing, Hubei University of Arts and Sciences, Xiangyang 441053, Hubei, China)

Received:2023-10-10 Revised:2024-04-26 Published:2024-09-25

摘要/Abstract

摘要： 电池荷电状态(SOC)估算是电池管理系统(BMS)的关键技术之一。针对锂电池提出了一种基于猫群(CSO)算法和自适应无迹卡尔曼滤波(AUKF)算法相结合的电池SOC估算方法；建立了基于二阶RC等效电路模型的锂电池状态方程，采用CSO算法提高电池辨识精度，联合AUKF算法对SOC进行估算；基于混合脉冲功率测试工况(HPPC)和间歇恒流放电工况下的数据对该方法有效性进行了验证。研究结果表明：基于CSO-AUKF估算，SOC最大误差小于1.64%，估算精度及稳定性均好于遗传算法。

关键词: 车辆工程；锂电池汽车；荷电状态(SOC)；猫群(CSO)算法；自适应无迹卡尔曼滤波(AUKF)算法

Abstract: Battery state of charge (SOC) estimation is one of the key technologies of battery management system (BMS). A battery SOC estimation method based on the combination of cat swarm optimization (CSO) algorithm and adaptive unscented Kalman filtering (AUKF) algorithm was proposed for lithium-ion batteries. The state equation of lithium battery based on the second-order RC equivalent circuit model was established, and the CSO algorithm was used to improve the identification accuracy of battery. The AUKF algorithm was combined to estimate the SOC. Based on the data of hybrid pulse power characterization test (HPPC) condition and intermittent constant current discharge condition, the effectiveness of the proposed method was verified. The results show that based on CSO-AUKF estimation, the maximum error of SOC is less than 1.64%, and the estimation accuracy and stability are both better than those of the genetic algorithm.

Key words: vehicle engineering; lithium battery vehicles; state of charge (SOC); cat swarm optimization (CSO) algorithm; adaptive unscented Kalman filtering (AUKF) algorithm

中图分类号:

吴华伟1, 2，洪强1, 2，陈运星1, 2，马毓博1, 2. 基于CSO-AUKF的锂电池SOC估算方法[J]. 重庆交通大学学报（自然科学版）, 2024, 43(9): 118-126.

WU Huawei1,2, HONG Qiang1,2, CHEN Yunxing1,2, MA Yubo1,2. Lithium Battery SOC Estimation Method Based on CSO-AUKF[J]. Journal of Chongqing Jiaotong University(Natural Science), 2024, 43(9): 118-126.

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基于CSO-AUKF的锂电池SOC估算方法

Lithium Battery SOC Estimation Method Based on CSO-AUKF

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