基于BP神经网络与H∞滤波的锂电池SoH-SoC联合估计研究  

Joint estimation of SoH-SoC for lithium battery based on BP neural network and H infinity filter

作　　者：钱伟[1,2] 王亚丰 王晨 郭向伟 赵大中 Qian Wei;Wang Yafeng;Wang Chen;Guo Xiangwei;Zhao Dazhong(School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454003,China;Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment,Jiaozuo 454003,China)

机构地区：[1]河南理工大学电气工程与自动化学院,焦作454003 [2]河南省煤矿装备智能检测与控制重点实验室,焦作454003

出　　处：《仪器仪表学报》2024年第6期307-319,共13页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金项目(62373137);河南省高校重点科研项目(23A470006)资助。

摘　　要：锂电池健康状态(SoH)和荷电状态(SoC)的精确估计是新能源汽车安全运行的重要保障。针对SoH-SoC联合估计精度低、鲁棒性差的问题,提出一种基于变学习率BP神经网络和自适应渐消扩展H∞滤波的SoH-SoC联合估计方法。首先,提出一种基于单位充电压差时间间隔的新型SoH特征参数;其次,通过设计新型变学习率BP神经网络,提高传统BP网络误差收敛速度及缩短权值寻优时间;最后,通过设计新型自适应衰减因子对传统扩展H∞滤波误差协方差矩阵进行加权,建立自适应渐消扩展H∞滤波算法,减小陈旧量测值对估计结果的影响,提高扩展H∞滤波的估计精度及鲁棒性。实验结果表明,本文所提算法SoH估计误差小于0.35%,SoC估计误差小于0.5%,展现出较高的估计精度和鲁棒性。Accurate estimation of the lithium batteries′state of health(SoH)and state of charge(SoC)is an important guarantee for the safe operation of new energy vehicles.Aiming at the low accuracy and poor robustness problems of joint SoH-SoC estimation,a joint SoH-SoC estimation method based on BP neural network with variable learning rate and adaptive fading extended H∞filter is proposed.Firstly,a novel SoH feature parameter based on time interval of unit charging voltage difference is proposed.Secondly,the traditional BP neural network is improved by using a novel BP neural network with variable learning rate to improve the error convergence speed and shorten the weights optimization search time.Finally,by designing a new type of adaptive fading factor to weight the error covariance matrix of traditional extended H infinity filter,an adaptive fading extended H infinity filter algorithm is established to reduce the influence of stale measurement on the estimation results and correspondingly improve the estimation accuracy and robustness of extended H infinity filter.The experimental results show that the SoH and SoC estimation errors of the proposed algorithm are smaller in this paper is less than 0.35%and SoC estimation error is less than 0.5%,respectively,demonstrating the high estimation accuracy and robustness.

关 键 词：锂电池 健康状态 荷电状态 神经网络 自适应滤波 

分 类 号：TM912[电气工程—电力电子与电力传动] TH89[机械工程—仪器科学与技术]