Prediction of Lithium Battery Voltage and State of Charge Using Multi-Head Attention BiLSTM Neural Network
Predicting battery states such as the voltage and state of charge (SOC) can help us monitor lithium batteries more efficiently during usage. This study proposed a predictive model for the lithium battery voltage and SOC by combining a second-order RC equivalent circuit model with a multi-head attent...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/3011 |
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| author | Haiwen Xi Taolin Lv Jincheng Qin Mingsheng Ma Jingying Xie Shigang Lu Zhifu Liu |
| author_facet | Haiwen Xi Taolin Lv Jincheng Qin Mingsheng Ma Jingying Xie Shigang Lu Zhifu Liu |
| author_sort | Haiwen Xi |
| collection | DOAJ |
| description | Predicting battery states such as the voltage and state of charge (SOC) can help us monitor lithium batteries more efficiently during usage. This study proposed a predictive model for the lithium battery voltage and SOC by combining a second-order RC equivalent circuit model with a multi-head attention Bidirectional Long Short-Term Memory (MHA-BiLSTM) neural network. The equivalent circuit model simulates long-term charge–discharge cycles in Simulink, providing essential data for model training. The BiLSTM model, enhanced by the multi-head attention mechanism, is used for accurate short-term predictions of the battery voltage and SOC. The experimental results demonstrate that the proposed MHA-BiLSTM model outperforms other models in the prediction accuracy, achieving an R<sup>2</sup> of 0.91, with the lowest RMSE of 0.0567 and MAPE of 0.0095. This hybrid approach effectively captures the dynamic behavior of the battery and reduces predictive errors, making it a promising solution for battery health monitoring and management. |
| format | Article |
| id | doaj-art-839fad8a04594ec58c7dbb0eef26b260 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-839fad8a04594ec58c7dbb0eef26b2602025-08-20T02:11:25ZengMDPI AGApplied Sciences2076-34172025-03-01156301110.3390/app15063011Prediction of Lithium Battery Voltage and State of Charge Using Multi-Head Attention BiLSTM Neural NetworkHaiwen Xi0Taolin Lv1Jincheng Qin2Mingsheng Ma3Jingying Xie4Shigang Lu5Zhifu Liu6School of Microelectronics, Shanghai University, Shanghai 200444, ChinaShanghai Institute of Space Power-Sources, Shanghai 200245, ChinaShanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 201899, ChinaShanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 201899, ChinaShanghai Institute of Space Power-Sources, Shanghai 200245, ChinaInstitute for Sustainable Energy, Shanghai University, Shanghai 200444, ChinaSchool of Microelectronics, Shanghai University, Shanghai 200444, ChinaPredicting battery states such as the voltage and state of charge (SOC) can help us monitor lithium batteries more efficiently during usage. This study proposed a predictive model for the lithium battery voltage and SOC by combining a second-order RC equivalent circuit model with a multi-head attention Bidirectional Long Short-Term Memory (MHA-BiLSTM) neural network. The equivalent circuit model simulates long-term charge–discharge cycles in Simulink, providing essential data for model training. The BiLSTM model, enhanced by the multi-head attention mechanism, is used for accurate short-term predictions of the battery voltage and SOC. The experimental results demonstrate that the proposed MHA-BiLSTM model outperforms other models in the prediction accuracy, achieving an R<sup>2</sup> of 0.91, with the lowest RMSE of 0.0567 and MAPE of 0.0095. This hybrid approach effectively captures the dynamic behavior of the battery and reduces predictive errors, making it a promising solution for battery health monitoring and management.https://www.mdpi.com/2076-3417/15/6/3011lithium battery predictionequivalent circuit modelmulti-head attentionbi-directional long short-term memory |
| spellingShingle | Haiwen Xi Taolin Lv Jincheng Qin Mingsheng Ma Jingying Xie Shigang Lu Zhifu Liu Prediction of Lithium Battery Voltage and State of Charge Using Multi-Head Attention BiLSTM Neural Network Applied Sciences lithium battery prediction equivalent circuit model multi-head attention bi-directional long short-term memory |
| title | Prediction of Lithium Battery Voltage and State of Charge Using Multi-Head Attention BiLSTM Neural Network |
| title_full | Prediction of Lithium Battery Voltage and State of Charge Using Multi-Head Attention BiLSTM Neural Network |
| title_fullStr | Prediction of Lithium Battery Voltage and State of Charge Using Multi-Head Attention BiLSTM Neural Network |
| title_full_unstemmed | Prediction of Lithium Battery Voltage and State of Charge Using Multi-Head Attention BiLSTM Neural Network |
| title_short | Prediction of Lithium Battery Voltage and State of Charge Using Multi-Head Attention BiLSTM Neural Network |
| title_sort | prediction of lithium battery voltage and state of charge using multi head attention bilstm neural network |
| topic | lithium battery prediction equivalent circuit model multi-head attention bi-directional long short-term memory |
| url | https://www.mdpi.com/2076-3417/15/6/3011 |
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