A hybrid LSTM-transformer model for accurate
With the widespread application of lithium-ion batteries in electric vehicles and energy storage systems, health monitoring and remaining useful life prediction have become critical components of battery management systems. To address the challenges posed by the high nonlinearity and long-term depen...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Electronics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/felec.2025.1654344/full |
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| _version_ | 1849231519993823232 |
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| author | Tianren Zhao Yanhui Zhang Minghao Wang Wei Feng Shengxian Cao Gong Wang |
| author_facet | Tianren Zhao Yanhui Zhang Minghao Wang Wei Feng Shengxian Cao Gong Wang |
| author_sort | Tianren Zhao |
| collection | DOAJ |
| description | With the widespread application of lithium-ion batteries in electric vehicles and energy storage systems, health monitoring and remaining useful life prediction have become critical components of battery management systems. To address the challenges posed by the high nonlinearity and long-term dependency in battery degradation modeling, this paper proposes a deep hybrid architecture that integrates Long Short-Term Memory networks with Transformer mechanisms, aiming to improve the accuracy and robustness of RUL prediction. Firstly, time-series samples are constructed from raw battery data, and physically consistent temperature-derived features—including average temperature, temperature range, and temperature fluctuation—are engineered. Data preprocessing is performed using standardization and Yeo-Johnson transformation. The model employs LSTM modules to capture local temporal patterns, while the Transformer modules extract global dependencies through multi-head self-attention mechanisms. These complementary features are fused to enable joint modeling of battery health states. The regression task is optimized using the Mean Squared Error loss function and trained with the Adam optimizer. Experimental results on the MIT battery dataset demonstrate the proposed model achieves excellent performance in a 7-step multi-point prediction task, with a Root Mean Square Error of 0.0085, Mean Absolute Percentage Error of 0.0200, and a coefficient of determination of 0.9902. Compared with alternative models such as MC-LSTM and XGBoost-LSTM, the proposed model exhibits superior accuracy and stability. Residual analysis and visualization further confirm the model’s unbiased and stable predictive capability. This study shows that the LSTM-Transformer hybrid architecture offers significant potential in modeling complex battery degradation processes and enhancing RUL prediction accuracy, providing effective technical support for the development of intelligent battery health management systems. |
| format | Article |
| id | doaj-art-9dc47da02e604688808e8300abaac47d |
| institution | Kabale University |
| issn | 2673-5857 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Electronics |
| spelling | doaj-art-9dc47da02e604688808e8300abaac47d2025-08-21T05:27:39ZengFrontiers Media S.A.Frontiers in Electronics2673-58572025-08-01610.3389/felec.2025.16543441654344A hybrid LSTM-transformer model for accurateTianren Zhao0Yanhui Zhang1Minghao Wang2Wei Feng3Shengxian Cao4Gong Wang5Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaUniversity of Macau, Taipa, Macao SAR, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaNortheast Electric Power University, Jilin, ChinaNortheast Electric Power University, Jilin, ChinaWith the widespread application of lithium-ion batteries in electric vehicles and energy storage systems, health monitoring and remaining useful life prediction have become critical components of battery management systems. To address the challenges posed by the high nonlinearity and long-term dependency in battery degradation modeling, this paper proposes a deep hybrid architecture that integrates Long Short-Term Memory networks with Transformer mechanisms, aiming to improve the accuracy and robustness of RUL prediction. Firstly, time-series samples are constructed from raw battery data, and physically consistent temperature-derived features—including average temperature, temperature range, and temperature fluctuation—are engineered. Data preprocessing is performed using standardization and Yeo-Johnson transformation. The model employs LSTM modules to capture local temporal patterns, while the Transformer modules extract global dependencies through multi-head self-attention mechanisms. These complementary features are fused to enable joint modeling of battery health states. The regression task is optimized using the Mean Squared Error loss function and trained with the Adam optimizer. Experimental results on the MIT battery dataset demonstrate the proposed model achieves excellent performance in a 7-step multi-point prediction task, with a Root Mean Square Error of 0.0085, Mean Absolute Percentage Error of 0.0200, and a coefficient of determination of 0.9902. Compared with alternative models such as MC-LSTM and XGBoost-LSTM, the proposed model exhibits superior accuracy and stability. Residual analysis and visualization further confirm the model’s unbiased and stable predictive capability. This study shows that the LSTM-Transformer hybrid architecture offers significant potential in modeling complex battery degradation processes and enhancing RUL prediction accuracy, providing effective technical support for the development of intelligent battery health management systems.https://www.frontiersin.org/articles/10.3389/felec.2025.1654344/fulllithium-ion batteryremaining useful lifeLSTMtransformertime-series prediction |
| spellingShingle | Tianren Zhao Yanhui Zhang Minghao Wang Wei Feng Shengxian Cao Gong Wang A hybrid LSTM-transformer model for accurate Frontiers in Electronics lithium-ion battery remaining useful life LSTM transformer time-series prediction |
| title | A hybrid LSTM-transformer model for accurate |
| title_full | A hybrid LSTM-transformer model for accurate |
| title_fullStr | A hybrid LSTM-transformer model for accurate |
| title_full_unstemmed | A hybrid LSTM-transformer model for accurate |
| title_short | A hybrid LSTM-transformer model for accurate |
| title_sort | hybrid lstm transformer model for accurate |
| topic | lithium-ion battery remaining useful life LSTM transformer time-series prediction |
| url | https://www.frontiersin.org/articles/10.3389/felec.2025.1654344/full |
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