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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/6/3011 |
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| Summary: | 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. |
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| ISSN: | 2076-3417 |