Remaining-capacity estimation of lithium-ion batteries using heatmap-based denoising autoencoder and vision transformer algorithm
Lithium-ion batteries, which are valued for their high energy density and reusability, gradually lose capacity as charge–discharge cycles accumulate. Remaining capacity—the usable charge expressed in ampere-hours—is a direct, real-time indicator of state of health (SOH) and is critical for safe, eff...
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| Veröffentlicht in: | JOURNAL OF POWER ELECTRONICS Jg. 25; H. 9; S. 1745 - 1760 |
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Springer Nature Singapore
01.09.2025
Springer Nature B.V 전력전자학회 |
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| Abstract | Lithium-ion batteries, which are valued for their high energy density and reusability, gradually lose capacity as charge–discharge cycles accumulate. Remaining capacity—the usable charge expressed in ampere-hours—is a direct, real-time indicator of state of health (SOH) and is critical for safe, efficient battery management. We propose a deep learning framework that transforms multichannel time-series measurements into correlation heatmaps, which are then fed into a denoising autoencoder (DAE) to suppress sensor noise while preserving electrochemical patterns. These image-based representations are subsequently processed by a vision transformer (ViT) to estimate the remaining capacity. The ViT architecture captures complex spatial–temporal dependencies through self-attention mechanisms. Experiments on the NASA lithium-ion benchmark dataset show that the proposed heatmap-based DAE-ViT model achieves lower root mean square error and mean absolute error compared with recurrent neural networks, one-dimensional transformer encoders trained on DAE features, support vector regression, and XGBoost. To further validate the proposed model, we apply it to the CALCE CS2 dataset, where it also demonstrates robust performance under different degradation scenarios. Results demonstrate that image-based self-attention can deliver scalable, high-precision remaining-capacity estimation for lithium-ion battery management systems. |
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| AbstractList | Lithium-ion batteries, which are valued for their high energy density and reusability, gradually lose capacity as charge–discharge cycles accumulate. Remaining capacity—the usable charge expressed in ampere-hours—is a direct, real-time indicator of state of health (SOH) and is critical for safe, efficient battery management. We propose a deep learning framework that transforms multichannel time-series measurements into correlation heatmaps, which are then fed into a denoising autoencoder (DAE) to suppress sensor noise while preserving electrochemical patterns. These image-based representations are subsequently processed by a vision transformer (ViT) to estimate the remaining capacity. The ViT architecture captures complex spatial–temporal dependencies through self-attention mechanisms. Experiments on the NASA lithium-ion benchmark dataset show that the proposed heatmap-based DAE-ViT model achieves lower root mean square error and mean absolute error compared with recurrent neural networks, one-dimensional transformer encoders trained on DAE features, support vector regression, and XGBoost. To further validate the proposed model, we apply it to the CALCE CS2 dataset, where it also demonstrates robust performance under different degradation scenarios. Results demonstrate that image-based self-attention can deliver scalable, high-precision remaining-capacity estimation for lithium-ion battery management systems. Lithium-ion batteries, which are valued for their high energy density and reusability, gradually lose capacity as charge–discharge cycles accumulate. Remaining capacity—the usable charge expressed in ampere-hours—is a direct, real-time indicator of state of health (SOH) and is critical for safe, efficient battery management. We propose a deep learning framework that transforms multichannel time-series measurements into correlation heatmaps, which are then fed into a denoising autoencoder (DAE) to suppress sensor noise while preserving electrochemical patterns. These image-based representations are subsequently processed by a vision transformer (ViT) to estimate the remaining capacity. The ViT architecture captures complex spatial–temporal dependencies through self-attention mechanisms. Experiments on the NASA lithium-ion benchmark dataset show that the proposed heatmap-based DAE-ViT model achieves lower root mean square error and mean absolute error compared with recurrent neural networks, one-dimensional transformer encoders trained on DAE features, support vector regression, and XGBoost. To further validate the proposed model, we apply it to the CALCE CS2 dataset, where it also demonstrates robust performance under different degradation scenarios. Results demonstrate that image-based selfattention can deliver scalable, high-precision remaining-capacity estimation for lithium-ion battery management systems. KCI Citation Count: 0 |
| Author | Jang, Yunseo Park, Joung-Hu |
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| SubjectTerms | Aging Back propagation Batteries Datasets Deep learning Electrical Machines and Networks Engineering Kalman filters Lithium Lithium-ion batteries Machine learning Maintenance costs Noise reduction Original Article Parameter identification Power Electronics Power management Real time Recurrent neural networks Support vector machines 전기공학 |
| Title | Remaining-capacity estimation of lithium-ion batteries using heatmap-based denoising autoencoder and vision transformer algorithm |
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