AUTHOR=Guangxiong Meng , Zhongnan Liang , Zhongyi Mou 

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

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1459027

DOI=10.3389/fenrg.2024.1459027

ISSN=2296-598X

ABSTRACT=Abstract：With the popularity of battery-powered products such as electric vehicles and wearable devices, the prediction of remaining life of lithium batteries has become increasingly important. This study proposes a method based on the hybrid neural network for predicting the remaining life of lithium batteries. First, the variational modal decomposition is used for noise reduction processing to maximize the retention of the original information of capacity degradation.Second, the capacity declining trend after noise reduction is modeled and predicted by the combination of bi-directional long-short term memory and monte carlo dropout. Finally, experimental results show that the new method based on the VMD-MC-BiLSTM network achieves good performance in predicting the remaining life of lithium batteries and provides the confidence level, providing new ideas and methods for optimizing lithium battery management systems.