State of health of lithium ion battery estimation based on charging process

A series of electrochemical reactions will occur in the long-term use of lithium ion batteries, which will make the battery capacity decline, increase the internal resistance, and even cause catastrophic consequences such as explosion and fire. As an important part of battery management system (BMS), accurate estimation of state of health (SOH) can improve the performance, service life and safety of batteries. Different from the usual cycle times as input to estimate SOH, this paper takes the time interval of an equal charging voltage difference extracted from the constant current charging curve as the inputs, and analyzes the correlation between the selected feature and SOH by using the gray correlation degree method. Gaussian process regression algorithm is used to estimate SOH. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used to evaluate the estimation results, and it is showed that this method can estimate SOH with satisfying accuracy.