Prediction of remaining service life of lithium battery based on VMD-MC-BiLSTM

Abstract

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The growing popularity of battery-powered products, such as electric vehicles and wearable devices, has increasingly motivated the need to predict the remaining life of lithium-based batteries. This study proposes a method for predicting the remaining life of lithium-based batteries based on a hybrid neural network. First, variational modal decomposition (VMD) was used for noise reduction to maximize retention of the original information and prevent capacity degradation. Second, the trend of capacity decline after noise reduction was modeled and predicted using the combination of bidirectional long short-term memory (BiLSTM) and Monte Carlo (MC) dropout. Finally, experiments were conducted to show that the new method based on the VMD-MC-BiLSTM network achieves good performance for predicting the remaining life of a lithium battery with sufficient confidence level, thereby providing a new approach for optimizing the management of lithium batteries.

Keywords

• lithium battery
• remaining useful life prediction
• VMD-MC-BiLSTM network
• deep learning
• early diagnosis