Prediction of SOH and RUL for lithium battery based on Transformer combination model
CHANG Wei
HU Zhichao
PAN Duozhao
SHI Jiwen
Abstract：This paper employs a combined model of the Transformer model and various deep learning models to predict the SOH(State of Health)and RUL(Remaining Useful Life)of a battery.The model was tested on NASA's public data set,using current,voltage,and temperature to predict SOH,and current,resistance,and impedance to predict RUL.The model first uses a Convolutional Neural Network(CNN)model to extract the spatial features of the input data,and then uses a Bidirectional Long Short Term Memory(BiLSTM)model to extract the time series variation pattern of the input data.Then,the Transformer model's multi head attention mechanism and feedforward network is used to learn the feature representation of the input data.Finally,the attention mechanism is used to further select important parts of the spatiotemporal features of the input data and jointly predict SOH and RUL.Experiments on test data show that the Root Mean Square Error(RMSE)of SOH prediction reaches 0.084 85,and the mean square error of RUL prediction reaches 1.46,both of which are better than traditional methods.
Keywords：state of healthremaining useful lifeconvolution neural networkbidirectional long short-term memory
Publication Date：2024-08-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 184-190,198 )