ArticleScientific reports2021
Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multi-scale temporal convolution attention network for state-of-charge estimation in Li-ion batteries.Scientific reports · 2026Article
- A case study to compare the methods of SOC estimation for lithium-ion batteries using second-order RC circuit model.Scientific reports · 2026Article
- AE-BPNN: autoencoder and backpropagation neural network-based model for lithium-ion battery state of health estimation.Scientific reports · 2025Article
- State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms.Sensors (Basel, Switzerland) · 2025Article
- Enhanced SOC estimation of lithium ion batteries with RealTime data using machine learning algorithms.Scientific reports · 2024Article
- Forward layer-wise learning of convolutional neural networks through separation index maximizing.Scientific reports · 2024Article
- A power allocation strategy for fuel cell ship considering fuel cell performance difference.Scientific reports · 2023Article
- Efficient virtual high-content screening using a distance-aware transformer model.Journal of cheminformatics · 2023Article
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Authors and funding
11 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.