An Online Estimation Method of State of Health for Lithium-Ion Batteries Based on Constant Current Charging Curve

Abstract

Accurate estimation of state of health (SOH) is of great significance for the safety and reliability of lithium-ion batteries. In this paper, a novel method to estimate SOH online based on constant current charging curve is presented. In order to incorporate the factor of rates, a simple two-step data transformation process is carried out to make the method suitable for SOH estimation at different charging rates. Then polynomial is used to fit the transformed curve, and the coefficient sets of analytic expression obtained by fitting are taken as the battery aging feature variables. Finally, linear regression algorithm, the simplest machine learning algorithm, is employed to construct the mapping between feature variables and SOH, thus accomplishing the SOH estimation. When estimating SOH, only the charging curve of the whole constant current charging process is needed, regardless of the charging process at whatever rates. This method takes low computational cost, making it suitable for online estimation. The verification results on battery test data show that the method is of high accuracy and effectiveness.