Health state prediction of lithium-ion batteries based on XGBoost algorithm

The state of health of lithium-ion batteries is an extremely important indicator for the safe operation of electrical equipment. In order to solve the problem that the capacity of Li-ion batteries is difficult to measure online, this paper proposes an online method based on the XGBoost algorithm to estimate the state of health (SOH). First, health characteristics are extracted from the discharge voltage profile to characterise the capacity of the Li-ion battery. XGBoost is then used to predict SOH based on capacity drop. results show that the proposed method is able to estimate SOH with a root mean square error of less than 1.2% respectively. Furthermore, the proposed method was used for model training and prediction in less than 1 second.