State of Charge Estimation for Sodium-Ion Batteries Based on LSTM Network and Unscented Kalman Filter
With the increasing application of sodium-ion batteries in energy storage systems, accurate state of charge (SOC) estimation plays a vital role in ensuring both available battery capacity and operational safety. Traditional Kalman-filter-based methods often suffer from limited model expressiveness o...
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| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/11/7/274 |
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| author | Xiangang Zuo Xiaoheng Fu Xu Han Meng Sun Yuqian Fan |
| author_facet | Xiangang Zuo Xiaoheng Fu Xu Han Meng Sun Yuqian Fan |
| author_sort | Xiangang Zuo |
| collection | DOAJ |
| description | With the increasing application of sodium-ion batteries in energy storage systems, accurate state of charge (SOC) estimation plays a vital role in ensuring both available battery capacity and operational safety. Traditional Kalman-filter-based methods often suffer from limited model expressiveness or oversimplified physical assumptions, making it difficult to balance accuracy and robustness under complex operating conditions, which may lead to unreliable estimation results. To address these challenges, this paper proposes a hybrid framework that combines an unscented Kalman filter (UKF) with a long short-term memory (LSTM) neural network for SOC estimation. Under various driving conditions, the UKF—based on a second-order equivalent circuit model with online parameter identification—provides physically interpretable estimates, while LSTM effectively captures complex temporal dependencies. Experimental results under CLTC, NEDC, and WLTC cycles demonstrate that the proposed LSTM-UKF approach reduces the mean absolute error (MAE) by an average of 2% and the root mean square error (RMSE) by an average of 3% compared to standalone methods. The proposed framework exhibits excellent adaptability across different scenarios, offering a precise, stable, and robust solution for SOC estimation in sodium-ion batteries. |
| format | Article |
| id | doaj-art-0f619f198b8a437ea5c83dac0ded153e |
| institution | DOAJ |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-0f619f198b8a437ea5c83dac0ded153e2025-08-20T02:45:37ZengMDPI AGBatteries2313-01052025-07-0111727410.3390/batteries11070274State of Charge Estimation for Sodium-Ion Batteries Based on LSTM Network and Unscented Kalman FilterXiangang Zuo0Xiaoheng Fu1Xu Han2Meng Sun3Yuqian Fan4School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaWith the increasing application of sodium-ion batteries in energy storage systems, accurate state of charge (SOC) estimation plays a vital role in ensuring both available battery capacity and operational safety. Traditional Kalman-filter-based methods often suffer from limited model expressiveness or oversimplified physical assumptions, making it difficult to balance accuracy and robustness under complex operating conditions, which may lead to unreliable estimation results. To address these challenges, this paper proposes a hybrid framework that combines an unscented Kalman filter (UKF) with a long short-term memory (LSTM) neural network for SOC estimation. Under various driving conditions, the UKF—based on a second-order equivalent circuit model with online parameter identification—provides physically interpretable estimates, while LSTM effectively captures complex temporal dependencies. Experimental results under CLTC, NEDC, and WLTC cycles demonstrate that the proposed LSTM-UKF approach reduces the mean absolute error (MAE) by an average of 2% and the root mean square error (RMSE) by an average of 3% compared to standalone methods. The proposed framework exhibits excellent adaptability across different scenarios, offering a precise, stable, and robust solution for SOC estimation in sodium-ion batteries.https://www.mdpi.com/2313-0105/11/7/274SOC estimationlong short-term memory networkunscented Kalman filterequivalent circuit model |
| spellingShingle | Xiangang Zuo Xiaoheng Fu Xu Han Meng Sun Yuqian Fan State of Charge Estimation for Sodium-Ion Batteries Based on LSTM Network and Unscented Kalman Filter Batteries SOC estimation long short-term memory network unscented Kalman filter equivalent circuit model |
| title | State of Charge Estimation for Sodium-Ion Batteries Based on LSTM Network and Unscented Kalman Filter |
| title_full | State of Charge Estimation for Sodium-Ion Batteries Based on LSTM Network and Unscented Kalman Filter |
| title_fullStr | State of Charge Estimation for Sodium-Ion Batteries Based on LSTM Network and Unscented Kalman Filter |
| title_full_unstemmed | State of Charge Estimation for Sodium-Ion Batteries Based on LSTM Network and Unscented Kalman Filter |
| title_short | State of Charge Estimation for Sodium-Ion Batteries Based on LSTM Network and Unscented Kalman Filter |
| title_sort | state of charge estimation for sodium ion batteries based on lstm network and unscented kalman filter |
| topic | SOC estimation long short-term memory network unscented Kalman filter equivalent circuit model |
| url | https://www.mdpi.com/2313-0105/11/7/274 |
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