Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter. (27th March 2021)

• Record Type: Journal Article
• Authors: Li, Jiabo; Ye, Min; Gao, Kangping; Xu, Xinxin; Wei, Meng; Jiao, Shengjie
• Is Part Of: International journal of energy research. Volume 45:Number 9(2021)
• Page Start: 13307
• Page End: 13322
• Publication Date: 2021-03-27
• Subjects: dynamic window -- improved dual‐adaptive extended Kalman filter (IDAEKF) -- lithium‐ion battery (LIB) -- state of charge (SOC) -- state of health (SOH) -- error model; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.6658
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: Lithium‐ion batteries (LIBs) are widely used in electric vehicles due to its high energy density and low pollution. As the key monitoring parameters of battery management system (BMS), accurate estimation of the state of charge (SOC) and state of health (SOH) can promote the utilization rate of battery, which is of great significance to ensure the safe use of LIBs. In this paper, a novel dual Kalman filter method is proposed to achieve simultaneous SOC and SOH estimation. This paper improves the estimation accuracy of SOC and SOH from the following four aspects. Firstly, the widely used equivalent circuit model is established as the battery model in this paper, and the forgetting factor recursive least squares (FFRLS) method is applied to identify the model parameters. Secondly, two kinds of single‐variable battery states are established to analyze the influence of OCV‐SOC curve and battery capacity on SOC estimation. Based on this, an error model is proposed combined with Kalman filter to achieve better estimation results of SOC and SOH. Besides, to promote the accuracy of SOC estimation, based on the error innovation sequence (EIS) and residual innovation sequence (RIS), the improved dual adaptive extended Kalman filter (IDAEKF) algorithm based on dynamic window is proposed. Finally, the superiority of the proposed model is verified under different cycles. Experimental results show that the estimation error of SOC and SOH is controlled within 1%.