Method of DCAE-Transformer Prediction for the Health State of Lithium-Ion Batteries
A novel model of DCAE-Transformer is introduced in this study,grounded in the Transformer architecture.It is designed to enhance the precision of state of health(SOH)estimation.Pearson correlation coefficient is employed for feature selection,denoising autoencoder(DAE)and convolutional neural network(CNN)amalgamation is integrated for data preprocessing and feature extraction.Subsequently,data is fed into the transformer framework for prediction.The validation conducted by using the datasets from NASA and CALCE demonstrates the metrics of exceptional performance for the DCAE-Transformer model:error indices(EMA,EMAP,and ERMS)on NASA battery samples remain below 1%,with the values of R-squared surpassing 99.5%;on CALCE samples,error metrics remain under 5%,with R-squared values exceeding 98%.These findings underscore the superior precision of the model and the generalizability in lithium battery SOH estimation.
lithium batterystate of health estimationconvolutional denoising autoencoder(DCAE)Transformerpredictive performance
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