LITHIUM ION BATTERY REMAINING LIFE PREDICTION METHOD BASED ON WOLF PACK OPTIMIZATION LSTM NETWORK

摘要

Provided is a lithium ion battery remaining life prediction method based on wolf pack optimization LSTM network, relating to the technical field of lithium ion batteries. The method first acquires monitoring data of a lithium ion battery, and extracts lithium ion battery capacity data from the monitoring data; determines a long short-term memory network structure, and constructs an LSTM-based lithium ion battery remaining life prediction model; then optimizes key parameters in the lithium ion battery remaining life direct prediction model using a wolf pack algorithm to obtain a direct prediction model based on a wolf pack optimization LSTM network; determines an optimal lithium ion battery remaining life direct prediction model using the optimization data; finally predicts later-stage lithium ion battery capacity data using the optimal lithium ion battery remaining life direct prediction model. The lithium ion battery remaining life prediction method based on wolf pack optimization LSTM network can accurately predict the remaining life of the lithium ion battery.