The prediction of the remaining useful life of battery is crucial for the safety and sustainable development of energy.This article proposes a prediction method for the remaining useful life of battery,using historical operating data and charging and discharging cycles of battery to construct a Bi-LSTM-Dropout network model.Using Bi-LSTM to extract long-term dependent features of battery in time series,using Dropout optimization algorithm to reduce the complexity of Bi LSTM network model and improve its generalization ability.The experimental results show that the accuracy of this method on the test set reaches 96.2%,achieving accurate prediction of the remaining useful life of the battery.
维普
万方数据
