Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

M A Hannan,Z Y Dong,K S M Sahari,M S Hossain Lipu,T M Indra Mahlia,F Blaabjerg,D N T How,K M Muttaqi,Pin Jern Ker,S K Tiong,M Mansor

doi:10.1038/s41598-021-98915-8

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.

Highlights

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 the first two subsections, we highlight the estimation accuracy of the proposed model trained at room temperature and varying ambient temperatures and compare the estimation robustness against other deep learning (DL) models
We study the influence of pre-training on the model and show that the pre-trained model

Summary

Introduction

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. More researchers are shifting their attention to using the data-driven approach for SOC estimation In this approach, the SOC is directly modeled from observable signals such as voltage, current and temperature of the Li-ion battery cell sampled over diverse operating conditions across different cell chemistry and m anufacturers[19]. DL can be used to directly approximate the relationship between the measurable cell signals (voltage, current, temperature) and the SOC with no additional processing such as using adaptive filters[30] This eliminates the needs of manual feature engineering which can take a considerable amount of time and expert domain knowledge and still produce accurate SOC estimation results[31]. The primary motivations for the proposal in this article are (i) shortcomings of the SL training framework, (ii) Inadequate validation and testing of transfer learning capabilities in DL models, (iii) shortcomings of the DL recurrent architectures and (iv) Emergence and success of the Transformer DL model in other domains

Methods

Results

Conclusion