Reliable Life Prediction and Evaluation Analysis of Lithium-ion Battery Based on Long-Short Term Memory Model

Lithium-ion batteries are widely used in portable electronic equipment, vehicles, and aerospace. The life and reliability of lithium-ion batteries are directly related to the performance and safety of electric drive products. It is of great practical significance to study lithium-ion batteries. Deep learning technology has strong data structure mining ability. Long-short Term Memory (LSTM) neural network is more suitable for solving serialized data problems. Therefore, in this paper, based on the capacity degradation data of lithiumion battery, the fault prediction model based on LSTM neural network is designed to obtain the pseudo-failure life when the failure threshold is reached. Through statistical analysis of pseudo-failure life data, predicting and evaluating reliable life, and finally obtaining reliability indicators such as reliability function, it is of great significance to ensure the good performance and state safety of lithium-ion batteries.