A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries  

作　　者：Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO 

机构地区：[1]Faculty of Computing,Harbin Institute of Technology,Harbin 150001,China [2]School of Electrical Engineering,Liaoning University of Technology,Jinzhou 121004,China [3]School of Computer Science,Northeast Electric Power University,Jilin 132000,China [4]Department of Medical Informatics,School of Biomedical Engineering and Informatics,Nanjing Medical University,Nanjing 211166,China [5]School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China

出　　处：《Frontiers of Computer Science》2024年第5期1-10,共10页计算机科学前沿（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(Grant Nos.62102191,61872114,and 61871020).

摘　　要：Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management systems.Deep learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series data.However,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be improved.To address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL prediction.The MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level features.Additionally,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL predictions.The experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation process.Our code and dataset are available at the website of github.

关 键 词：lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts 

分 类 号：TM912[电气工程—电力电子与电力传动]