Estimation of State-of-Health for Lithium-Ion Battery Based on Increment Capacity Analysis Method and Long Short-Term Memory Neural Network

In order to solve the problem that it is difficult to accurately evaluate the health status of lithium-ion batteries, a capacity increment analysis method is proposed to extract the health characteristics of lithium batteries, and a lithium battery health estimation model is established based on LSTM. First, the capacity increment analysis method is used to calculate the battery charge and discharge data. Then, the battery health features are extracted from the calculation results, and the correlation between the health features and the battery health status is verified by correlation analysis. Then, based on the extracted health features, the LSTM model is introduced to estimate the health state of the battery. Finally, in the simulation analysis, by comparing with the estimation accuracy of the RNN method, it is verified that the proposed method has higher accuracy in the estimation of the state of health of lithium-ion batteries.