基于全新电热耦合模型的锂电池关键状态在线联合估计方法  

Online Joint Estimation Method for Key States of Lithium Battery Based on a New Electro-thermal Coupling Model

作　　者：刘芳[1] 刘新慧 苏卫星 王琬茹[1] 卜凡涛 LIU Fang;LIU Xinhui;SU Weixing;WANG Wanru;BU Fantao(Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems(Tiangong University),Xiqing District,Tianjin 300387,China;Neusoft Reach Automotive Technology,Co.,Ltd.,Liaoning Province,Shenyang 110000,China)

机构地区：[1]天津市自主智能技术与系统重点实验室(天津工业大学),天津市西青区300387 [2]东软睿驰汽车技术(沈阳)有限公司,辽宁省沈阳市110000

出　　处：《中国电机工程学报》2024年第S01期202-214,共13页PROCEEDINGS OF THE CHINESE SOCIETY FOR ELECTRICAL ENGINEERING

基　　金：国家重点研发计划项目(2021YFB2501800)。

摘　　要：面向电动汽车一类宽温度,大幅值、宽频率随机电流应用场景,提出一种基于全新电热耦合模型的锂电池多状态在线联合估计方法。该模型由自回归等效电路模型与单态集总热模型耦合而成,以提高模型电气动态跟随性能。电热耦合模型参数采取“先验信息初始化-在线修正”的方式确定,以避免电池一致性问题带来的误差,从而实现电热耦合关系在宽温度内的连续准确表达。基于所提出的ARST(autoregression-single state thermal model)耦合模型,该文采用双滤波算法实现锂电池多状态的在线联合估计,弥补目前电池3种及以上状态联合估计的稀缺问题。最后,在[0,50]℃,基于两个动态工况,将所提出的算法与两类基于模型的多状态联合估计算法进行比较。结果表明:ARST模型具有更好的电气跟随性能;所提出的模型参数在线辨识算法能够有效提高模型精度,从而提高多状态联合估计精度;在宽温度应用中,相较仅基于电模型的多状态联合估计算法,兼顾热状态估计的多状态联合估计算法能够有效提高电池状态的估计精度。To meet the requirements of wide temperature range,high amplitude and wide frequency random current scenarios for electric vehicles,this paper proposes a multi-state online joint estimation method for lithium batteries based on a new electro-thermal coupling model.This model is composed by coupling an autoregressive equivalent circuit model(AR-ECM)and a single state lumped thermal model(SSTM),aiming to improve the performance of the model in tracking electrical dynamic characteristics.The parameters of the electro-thermal coupling are determined by using the method of“priori information initialization-online correction”to avoid errors caused by battery consistency issues.In this way,the electro-thermal coupling relationship can be continuously and accurately expressed over a wide temperature range.Based on the proposed autoregression-single state thermal(ARST)model,the multi-state online joint estimation of lithium batteries is realized by using a dual-filter structure algorithm.This compensates for the scarcity issue about the current joint estimation of three or more states of batteries to some extent.Finally,within the temperature range of[0,50℃],the proposed algorithm is compared with two model-based battery multi-state joint estimation algorithms under two dynamic working conditions.Experimental results show that the proposed ARST model has better performance in tracking the electrical characteristics of the batteries.The online model parameter identification algorithm proposed can effectively improve the accuracy of the electro-thermal coupling model,and further improve the accuracy of the multi-state joint estimation of lithium battery.Within a wide temperature application range,compared with the multi-state joint estimation algorithm based only on the electrical model,the multi-state joint estimation algorithm considering the state of temperature can effectively improve the estimation accuracy of the battery state.

关 键 词：锂电池 电热耦合模型 多状态联合估计 双滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]