Integrated Framework of LSTM and Physical-Informed Neural Network for Lithium-Ion Battery Degradation Modeling and Prediction
Accurate prediction of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation. However, traditional deep learning approaches often suffer from challenges such as overfitting, limited generalization capability, and suboptimal prediction accuracy...
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MDPI AG
2025-07-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/6/7/149 |
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| author | Yan Ding Jinqi Zhu Yang Liu Dan Ning Mingyue Qin |
| author_facet | Yan Ding Jinqi Zhu Yang Liu Dan Ning Mingyue Qin |
| author_sort | Yan Ding |
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| description | Accurate prediction of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation. However, traditional deep learning approaches often suffer from challenges such as overfitting, limited generalization capability, and suboptimal prediction accuracy. To address these issues, this paper proposes a novel framework that combines a Long Short-Term Memory (LSTM) network with a Physics-Informed Neural Network (PINN), referred to as LSTM-PINN, for high-precision SOH estimation. The proposed framework models battery degradation using state-space equations and extracts latent temporal features. These features are further integrated into a Deep Hidden Temporal Physical Module (DeepHTPM), which incorporates physical prior knowledge into the learning process. This integration significantly enhances the model’s ability to accurately capture the complex dynamics of battery degradation. The effectiveness of LSTM-PINN is validated using two publicly available datasets based on graphite cathode materials (NASA and CACLE). Extensive experimental results demonstrate the superior predictive performance of the proposed model, achieving Mean Absolute Errors (MAEs) of just 0.594% and 0.746% and Root Mean Square Errors (RMSEs) of 0.791% and 0.897% on the respective datasets. Our proposed LSTM-PINN framework enables accurate battery aging modeling, advancing lithium-ion battery SOH prediction for industrial applications. |
| format | Article |
| id | doaj-art-6da22379d4d246dd82b9c204fe772f3c |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | AI |
| spelling | doaj-art-6da22379d4d246dd82b9c204fe772f3c2025-08-20T03:55:48ZengMDPI AGAI2673-26882025-07-016714910.3390/ai6070149Integrated Framework of LSTM and Physical-Informed Neural Network for Lithium-Ion Battery Degradation Modeling and PredictionYan Ding0Jinqi Zhu1Yang Liu2Dan Ning3Mingyue Qin4School of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, ChinaYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, ChinaSchool of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, ChinaSchool of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, ChinaSchool of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, ChinaAccurate prediction of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation. However, traditional deep learning approaches often suffer from challenges such as overfitting, limited generalization capability, and suboptimal prediction accuracy. To address these issues, this paper proposes a novel framework that combines a Long Short-Term Memory (LSTM) network with a Physics-Informed Neural Network (PINN), referred to as LSTM-PINN, for high-precision SOH estimation. The proposed framework models battery degradation using state-space equations and extracts latent temporal features. These features are further integrated into a Deep Hidden Temporal Physical Module (DeepHTPM), which incorporates physical prior knowledge into the learning process. This integration significantly enhances the model’s ability to accurately capture the complex dynamics of battery degradation. The effectiveness of LSTM-PINN is validated using two publicly available datasets based on graphite cathode materials (NASA and CACLE). Extensive experimental results demonstrate the superior predictive performance of the proposed model, achieving Mean Absolute Errors (MAEs) of just 0.594% and 0.746% and Root Mean Square Errors (RMSEs) of 0.791% and 0.897% on the respective datasets. Our proposed LSTM-PINN framework enables accurate battery aging modeling, advancing lithium-ion battery SOH prediction for industrial applications.https://www.mdpi.com/2673-2688/6/7/149state of healthphysical information neural networkdeep hidden temporal physical module |
| spellingShingle | Yan Ding Jinqi Zhu Yang Liu Dan Ning Mingyue Qin Integrated Framework of LSTM and Physical-Informed Neural Network for Lithium-Ion Battery Degradation Modeling and Prediction AI state of health physical information neural network deep hidden temporal physical module |
| title | Integrated Framework of LSTM and Physical-Informed Neural Network for Lithium-Ion Battery Degradation Modeling and Prediction |
| title_full | Integrated Framework of LSTM and Physical-Informed Neural Network for Lithium-Ion Battery Degradation Modeling and Prediction |
| title_fullStr | Integrated Framework of LSTM and Physical-Informed Neural Network for Lithium-Ion Battery Degradation Modeling and Prediction |
| title_full_unstemmed | Integrated Framework of LSTM and Physical-Informed Neural Network for Lithium-Ion Battery Degradation Modeling and Prediction |
| title_short | Integrated Framework of LSTM and Physical-Informed Neural Network for Lithium-Ion Battery Degradation Modeling and Prediction |
| title_sort | integrated framework of lstm and physical informed neural network for lithium ion battery degradation modeling and prediction |
| topic | state of health physical information neural network deep hidden temporal physical module |
| url | https://www.mdpi.com/2673-2688/6/7/149 |
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