Intelligent estimation on state of health of lithium-ion power batteries based on failure feature extraction

In order to provide an accurate and reliable effective state-of-health (SOH) estimation, a novel hybrid data-driven estimation method by failure feature extraction is proposed. Firstly, influencing factors which reflect the failure of lithium-ion power batteries are studied, and three failure features of lithium-ion power batteries used as inputs of the estimation model are extracted by fuzzy grey relational analysis (FGRA) method. Then, the improved Least Squares Support Vector Machine (LSSVM) model is employed to estimate the SOH under different ambient temperature conditions. The results show that CC charging time, CV charging capacity and CV charging average temperature are determined as the failure features of the SOH estimation model, whose correlation degree to the battery capacity are 0.8774, 0.8104 and 0.8771, respectively. Compared with SVM, the improved LSSVM model has higher SOH estimation accuracy for the lithium-ion power battery under different ambient temperature conditions. In addition, the SOH estimation curves basically matches the actual curves, where the SOH estimation errors are less than 0.02. Moreover, the mean square error accuracy of the prediction results is at the level of 0.00001, and the determination coefficient is between 0.92 and 0.997. This work provides reference for enhancing the SOH estimation performance and safety of lithium-ion power batteries.