Remaining‑capacity estimation of lithium‑ion batteries using heatmap‑based denoising autoencoder and vision transformer algorithm

Yunseo Jang; Joung-Hu Park

Remaining‑capacity estimation of lithium‑ion batteries using heatmap‑based denoising autoencoder and vision transformer algorithm

Yunseo Jang

Joung-Hu Park

Vol. 25, No. 9, pp. 1745-1760, Sep. 2025

10.1007/s43236-025-01134-x

Correlation heatmap

Denoising Autoencoder

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 selfattention can deliver scalable, high-precision remaining-capacity estimation for lithium-ion battery management systems.

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Cite this article

[IEEE Style]

Y. Jang and J. Park, "Remaining‑capacity estimation of lithium‑ion batteries using heatmap‑based denoising autoencoder and vision transformer algorithm," Journal of Power Electronics, vol. 25, no. 9, pp. 1745-1760, 2025. DOI: 10.1007/s43236-025-01134-x.

[ACM Style]

Yunseo Jang and Joung-Hu Park. 2025. Remaining‑capacity estimation of lithium‑ion batteries using heatmap‑based denoising autoencoder and vision transformer algorithm. Journal of Power Electronics, 25, 9, (2025), 1745-1760. DOI: 10.1007/s43236-025-01134-x.