SOH Estimation of Lithium Batteries Based on New Health Factors and Optimized BP Neural Networks

The study of the state of health (SOH) of lithium-ion batteries can avert major mishaps while also grading retired batteries for secondary use. To overcome the flaws of the traditional back propagation (BP) neural network technology, which has low precision and a poor fitting effect, this study proposes a model for forecasting the SOH of lithium-ion batteries based on a novel health factor and an upgraded BP algorithm. To begin, the NASA lithium battery database is evaluated offline, and the metrics with the highest correlation in the charging process are chosen as new health variables. On the other hand, simulation experiments are carried out on various groupings of battery data, and the BP algorithm is optimized using a genetic algorithm. The experimental results show that the errors in the classic BP models are all less than 2.4%, whereas the errors in the optimized BP neural network model are all less than 0.38%, and that the fitting effect has better generalization than the traditional BP model, which improves the estimation accuracy of SOH of lithium batteries, and that the proposed health factor can be directly collected by the battery management system (BMS), which has some practical implications.