A Collaborative Estimation Scheme for Lithium-Ion Battery State of Charge and State of Health Based on Electrochemical Model
Sheyin Wu, Wenjie Pan, Maotao Zhu
Abstract
Nowadays, many studies focus on single state of health (SOH) or state of charge (SOC) estimation. How to effectively combine the two for joint estimation is particularly important. This paper relies on our previous research foundation of SOH, and further increases the electrochemical model, and realizes the SOC estimation of the battery under different SOH stages by combining the two. An SPM model based on electrochemical mechanism is proposed and sensitivity analysis is performed for the parameters in the model. Three types of dynamic conditions, FUDS, BJDST and DST, are used to verify the feasibility of the model. Further, the synergistic estimation of SOH and SOC is realized by the change of the difference between the two lithium ion concentrations with the battery decay when SOC = 100% and SOC = 0% in the SPM model. In the offline state, a linear regression relationship is constructed between the results of the GPR model and the parameters of the SPM model. During online prediction, the parameters of SPM are obtained by calibrating the offline model, and the goal of estimating SOC based on SOH is realized. The experimental results show that the maximum error value of SOC estimation does not exceed 0.08, and the model has high accuracy.