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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/2504.03701 |
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| _version_ | 1866908302568325120 |
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| author | Li, Yuqi Zhang, Han Gui, Xiaofan Chen, Zhao Li, Yu Chi, Xiwen Zhou, Quan Zheng, Shun Lu, Ziheng Xu, Wei Bian, Jiang Chen, Liquan Li, Hong |
| author_facet | Li, Yuqi Zhang, Han Gui, Xiaofan Chen, Zhao Li, Yu Chi, Xiwen Zhou, Quan Zheng, Shun Lu, Ziheng Xu, Wei Bian, Jiang Chen, Liquan Li, Hong |
| contents | Battery degradation is governed by complex and randomized cyclic conditions, yet existing modeling and prediction frameworks usually rely on rigid, unchanging protocols that fail to capture real-world dynamics. The stochastic electrical signals make such prediction extremely challenging, while, on the other hand, they provide abundant additional information, such as voltage fluctuations, which may probe the degradation mechanisms. Here, we present chemistry-aware battery degradation prediction under dynamic conditions with machine learning, which integrates hidden Markov processes for realistic power simulations, an automated batch-testing system that generates a large electrochemical dataset under randomized conditions, an interfacial chemistry database derived from high-throughput X-ray photoelectron spectroscopy for mechanistic probing, and a machine learning model for prediction. By automatically constructing a polynomial-scale feature space from irregular electrochemical curves, our model accurately predicts both battery life and critical knee points. This feature space also predicts the composition of the solid electrolyte interphase, revealing six distinct failure mechanisms-demonstrating a viable approach to use electrical signals to infer interfacial chemistry. This work establishes a scalable and adaptive framework for integrating chemical engineering and data science to advance noninvasive diagnostics and optimize processes for more durable and sustainable energy storage technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Chemistry-aware battery degradation prediction under simulated real-world cyclic protocols Li, Yuqi Zhang, Han Gui, Xiaofan Chen, Zhao Li, Yu Chi, Xiwen Zhou, Quan Zheng, Shun Lu, Ziheng Xu, Wei Bian, Jiang Chen, Liquan Li, Hong Signal Processing Machine Learning Battery degradation is governed by complex and randomized cyclic conditions, yet existing modeling and prediction frameworks usually rely on rigid, unchanging protocols that fail to capture real-world dynamics. The stochastic electrical signals make such prediction extremely challenging, while, on the other hand, they provide abundant additional information, such as voltage fluctuations, which may probe the degradation mechanisms. Here, we present chemistry-aware battery degradation prediction under dynamic conditions with machine learning, which integrates hidden Markov processes for realistic power simulations, an automated batch-testing system that generates a large electrochemical dataset under randomized conditions, an interfacial chemistry database derived from high-throughput X-ray photoelectron spectroscopy for mechanistic probing, and a machine learning model for prediction. By automatically constructing a polynomial-scale feature space from irregular electrochemical curves, our model accurately predicts both battery life and critical knee points. This feature space also predicts the composition of the solid electrolyte interphase, revealing six distinct failure mechanisms-demonstrating a viable approach to use electrical signals to infer interfacial chemistry. This work establishes a scalable and adaptive framework for integrating chemical engineering and data science to advance noninvasive diagnostics and optimize processes for more durable and sustainable energy storage technologies. |
| title | Chemistry-aware battery degradation prediction under simulated real-world cyclic protocols |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2504.03701 |