中国电机工程学报2024，Vol.44Issue(z1)：202-214,13.DOI:10.13334/j.0258-8013.pcsee.232858
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
摘要
Abstract
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Key words
lithium-ion battery/electro-thermal model/multi-state joint estimation/dual-filter structure分类
信息技术与安全科学引用本文复制引用
刘芳,刘新慧,苏卫星,王琬茹,卜凡涛..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[J].中国电机工程学报,2024,44(z1):202-214,13.基金项目
国家重点研发计划项目(2021YFB2501800).National Key R&D Program of China(2021YFB2501800). （2021YFB2501800）
