Optimal Signal Decomposition-based Multi-Stage Learning for Battery Health Estimation
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929688938545152 |
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| author | Pamshetti, Vijay Babu Zhang, Wei Tseng, King Jet Ng, Bor Kiat Yan, Qingyu |
| author_facet | Pamshetti, Vijay Babu Zhang, Wei Tseng, King Jet Ng, Bor Kiat Yan, Qingyu |
| contents | Battery health estimation is fundamental to ensure battery safety and reduce cost. However, achieving accurate estimation has been challenging due to the batteries' complex nonlinear aging patterns and capacity regeneration phenomena. In this paper, we propose OSL, an optimal signal decomposition-based multi-stage machine learning for battery health estimation. OSL treats battery signals optimally. It uses optimized variational mode decomposition to extract decomposed signals capturing different frequency bands of the original battery signals. It also incorporates a multi-stage learning process to analyze both spatial and temporal battery features effectively. An experimental study is conducted with a public battery aging dataset. OSL demonstrates exceptional performance with a mean error of just 0.26%. It significantly outperforms comparison algorithms, both those without and those with suboptimal signal decomposition and analysis. OSL considers practical battery challenges and can be integrated into real-world battery management systems, offering a good impact on battery monitoring and optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16377 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal Signal Decomposition-based Multi-Stage Learning for Battery Health Estimation Pamshetti, Vijay Babu Zhang, Wei Tseng, King Jet Ng, Bor Kiat Yan, Qingyu Machine Learning Artificial Intelligence Battery health estimation is fundamental to ensure battery safety and reduce cost. However, achieving accurate estimation has been challenging due to the batteries' complex nonlinear aging patterns and capacity regeneration phenomena. In this paper, we propose OSL, an optimal signal decomposition-based multi-stage machine learning for battery health estimation. OSL treats battery signals optimally. It uses optimized variational mode decomposition to extract decomposed signals capturing different frequency bands of the original battery signals. It also incorporates a multi-stage learning process to analyze both spatial and temporal battery features effectively. An experimental study is conducted with a public battery aging dataset. OSL demonstrates exceptional performance with a mean error of just 0.26%. It significantly outperforms comparison algorithms, both those without and those with suboptimal signal decomposition and analysis. OSL considers practical battery challenges and can be integrated into real-world battery management systems, offering a good impact on battery monitoring and optimization. |
| title | Optimal Signal Decomposition-based Multi-Stage Learning for Battery Health Estimation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.16377 |