A Data Augmentation Method to Optimize Neural Networks for Predicting SOH of Lithium Batteries

Abstract

Neural Network is an excellent methodology for predicting lithium battery state of health (SOH). However, if the data amount is insufficient, the neural network will be overfitted, which decreass the prediction accuracy of SOH. To solve this issue, a data augmentation method based on random noise superposition is proposed. The original dataset is expanded in this approach, which enhances the neural network’s generalization ability. Moreover, random noises simulate capacity regeneration, capacity dips and sensor errors during the actual operation of lithium batteries, which also improves the adaptive and robustness of the SOH prediction method. The proposed method is validated on mainstream neural networks, including long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks. In terms of the results, the proposed data augmentation method effectively improves the neural network generalization ability and lithium battery SOH prediction accuracy.