RUL prediction of lithium-ion battery based on BiLSTM

Abstract

Abstract In this paper, in order to avoid the unavoidable degradation of the Remaining Useful Life(RUL) of lithium batteries in course of use, which may lead to the inability of lithium batteries to work normally or even cause safety accidents, finding a way to effectively predict the remaining life of lithium batteries in advance is significant. Therefore, the generator and discriminator models are constructed through a multilayer bidirectional length and Bidirectional Long Short-Term Memory(BiLSTM) model, which expands fewer data samples, making the sample data volume greatly improved, and avoiding the overfitting of the subsequent deep learning models under the fewer sample data. In BiLSTM, the hidden layer preserves the internal state of bidirectional data, and the key information is preserved and discarded through the internal Long Short-Term Memory(LSTM) module, so as not to cause any damage to the battery. information is preserved and discarded to avoid the problem of gradient disappearance that occurs when the data is dependent for a long time, and the GAN-BiLSTM model is suggested to deal with the issue of low precision and accuracy of deep learning methods with small samples.