Publication: Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model
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Date
2021
Authors
Hannan M.A.
How D.N.T.
Lipu M.S.H.
Mansor M.
Ker P.J.
Dong Z.Y.
Sahari K.S.M.
Tiong S.K.
Muttaqi K.M.
Mahlia T.M.I.
Journal Title
Journal ISSN
Volume Title
Publisher
Nature Research
Abstract
Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell. � 2021, The Author(s).
Description
article; deep learning; environmental temperature; human