biostudies-literature00390Hannan MAMinistry of Higher Education, Malaysia19541https://www.ebi.ac.uk/biostudies/studies/S-EPMC8486825biostudies-literatureUnknown11(1)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.Scientific reportsDeep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model.PMC848682520190101LRGSLipu MSHHannan MAMahlia TMIDong ZYBlaabjerg FMuttaqi KMSahari KSMHow DNTMansor MKer PJTiong SK39falseDeep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model.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.2021-01-01T00:00:00Z2021 Oct2024-11-07T11:49:43.32Z2022-02-11T11:46:46.188ZS-EPMC84868253459923310.1038/s41598-021-98915-8