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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.17414 |
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| _version_ | 1866918458130694144 |
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| author | Jiang, Chunlin Li, Hequn Deng, Zhongwei Shao, Jie Ning, Zhansheng |
| author_facet | Jiang, Chunlin Li, Hequn Deng, Zhongwei Shao, Jie Ning, Zhansheng |
| contents | Accurate prediction of lithium-ion battery capacity and its associated uncertainty is essential for reliable battery management but remains challenging due to the stochastic nature of aging. This paper presents a new method, termed the Conditional Diffusion U-Net with Attention (CDUA), which integrates feature engineering and deep learning to address this challenge. The proposed approach employs a diffusion-based generative model for time-series forecasting and incorporates attention mechanisms to enhance predictive performance. Battery capacity is first derived from real-world vehicle operation data. The most relevant features are then identified using the Pearson correlation coefficient and the XGBoost algorithm. These features are used to train the CDUA model, which comprises two components: (1) a contextual U-Net with self-attention to capture complex temporal dependencies, and (2) a noise predictor network that learns to estimate the added noise, enabling the reconstruction of accurate capacity values from noisy observations. Experimental validation on the real-world vehicle data demonstrates that the proposed CDUA model achieves a relative mean absolute error of 0.94% and a relative root mean square error of 1.14%, with a narrow 95% confidence interval of 3.74% in relative width. These results confirm that CDUA provides both accurate capacity estimation and reliable uncertainty quantification. Comparative experiments further verify its robustness and superior performance over existing mainstream approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17414 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conditional Diffusion Modeling with Attention for Probabilistic Battery Capacity Prediction under Real-World Condition Jiang, Chunlin Li, Hequn Deng, Zhongwei Shao, Jie Ning, Zhansheng Machine Learning Accurate prediction of lithium-ion battery capacity and its associated uncertainty is essential for reliable battery management but remains challenging due to the stochastic nature of aging. This paper presents a new method, termed the Conditional Diffusion U-Net with Attention (CDUA), which integrates feature engineering and deep learning to address this challenge. The proposed approach employs a diffusion-based generative model for time-series forecasting and incorporates attention mechanisms to enhance predictive performance. Battery capacity is first derived from real-world vehicle operation data. The most relevant features are then identified using the Pearson correlation coefficient and the XGBoost algorithm. These features are used to train the CDUA model, which comprises two components: (1) a contextual U-Net with self-attention to capture complex temporal dependencies, and (2) a noise predictor network that learns to estimate the added noise, enabling the reconstruction of accurate capacity values from noisy observations. Experimental validation on the real-world vehicle data demonstrates that the proposed CDUA model achieves a relative mean absolute error of 0.94% and a relative root mean square error of 1.14%, with a narrow 95% confidence interval of 3.74% in relative width. These results confirm that CDUA provides both accurate capacity estimation and reliable uncertainty quantification. Comparative experiments further verify its robustness and superior performance over existing mainstream approaches. |
| title | Conditional Diffusion Modeling with Attention for Probabilistic Battery Capacity Prediction under Real-World Condition |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.17414 |