Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method

• 作者: Rui Xiong Ju Wang Weixiang Shen Jinpeng Tian Hao Mu
• 来源期刊: 工程（英文）
• 年，卷（期）: 2021,（10）
• 所属期刊栏目: Energy Batteries-Article
• 页码范围: 1469-1482
• 页数: 14页
• 语种: 英文

摘要

Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in real-time.This study proposes a multi-stage model fusion algorithm to co-estimate SOC and capacity.Firstly,based on the assumption of a nor-mal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters.Secondly,a differential error gain with forward-looking ability is introduced into a proportional-integral observer(PlO)to accelerate convergence speed.Thirdly,a fusion algorithm is developed by combining a multi-stage model and proportional-integral-differential observer(PIDO)to co-estimate SOC and capacity under a complex application environment.Fourthly,the convergence and anti-noise performance of the fusion algorithm are discussed.Finally,the hardware-in-the-loop platform is set up to verify the per-formance of the fusion algorithm.The validation results of different aged LIBs over a wide range of tem-perature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2％and 3.3％,respectively.