基于分布式容积信息卡尔曼滤波的锂电池SOC估计  

Lithium Battery SOC Estimation Based on the Distributed Cubature Information Kalman Filter

作　　者：胡晓工 苏志坚 贾晓亮 吴光平 刘鹏 HU Xiao-gong;SU Zhi-jian;JIA Xiao-liang;WU Guang-ping;LIU Peng(Citic Electromechanical Manufacturing Group Co.,Ltd.,Houma 043015 China;School of Chemistry and Chemical Engineering,North University of China,Taiyuan 030051 China;Citic Electromechanical Manufacturing Group Co.,Ltd.,Regulatory Department,Houma 043015 China;School of Electrical and Control Engineering,North University of China,Taiyuan 030051 China)

机构地区：[1]中信机电制造集团有限公司,山西侯马043015 [2]中北大学化学与化工学院,山西太原030051 [3]中信机电制造集团有限公司监管代表室,山西侯马043015 [4]中北大学电气与控制工程学院,山西太原030051

出　　处：《自动化技术与应用》2024年第12期1-7,共7页Techniques of Automation and Applications

基　　金：国家自然科学基金项目(62373247);基础加强领域基金(2023-JCJQ-JJ-0353)。

摘　　要：锂电池作为一种重要的能量存储设备,可应用于电动汽车和可再生能源系统。其中电池管理系统(BMS)是确保电动汽车、电动工具及其他便携设备中锂电池安全、高效运行的关键组成部分。电池的状态估计(State of Charge,SOC)在BMS中占有重要地位。提出一种基于分布式容积信息卡尔曼滤波(Distributed Cubature Information Kalman Filter,DCIKF)的锂电池SOC估计算法,通过将卡尔曼滤波与分布式相结合,实现对SOC的高效率和高精度估计。与传统方法相比,该算法在降低计算复杂度的同时提高了SOC估计的精度,其分布式特点适用于大规模能源系统。通过实验验证,DCIKF算法在不同工况下都能够有效地估计锂电池的SOC,表明具有很好的实用性和稳健性。As an important energy storage device,lithium batteries can be applied to electric vehicles and renewable energy systems.The battery management system(BMS)is a key component to ensure the safe and efficient operation of lithium batteries in electric vehicles,power tools,and other portable devices.The state of charge(SOC)of the battery occupies an important position in the BMS.A novel SOC estimation algorithm based on distributed cubature information Kalman filter(DCIKF)is proposed to achieve high precision estimation of SOC by combining Kalman filter with cubic volume sampling.Compared with traditional methods,the algorithm improves the accuracy of SOC estimation while reducing the computational complexity.Its distributed characteristics is suitable for large-scale energy systems.Through experimental verification,the DCIKF algorithm can effectively estimate the SOC of lithium battery under different working conditions,indicating that it has good practicability and robustness.

关 键 词：锂电池 SOC估计 DCIKF算法 模型 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TM912[自动化与计算机技术—控制科学与工程]