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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/2509.26234 |
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| _version_ | 1866912965330993152 |
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| author | Patnaik, Ayush Fogelquist, Jackson Zufall, Adam B Ji, Yiwei Robinson, Stephen K Bai, Peng Lin, Xinfan |
| author_facet | Patnaik, Ayush Fogelquist, Jackson Zufall, Adam B Ji, Yiwei Robinson, Stephen K Bai, Peng Lin, Xinfan |
| contents | Lithium plating during fast charging is a critical degradation mechanism that accelerates capacity fade and can trigger catastrophic safety failures. Recent work has shown that plating onset can manifest in incremental-capacity analysis as an additional high-voltage feature above 4.0 V, often appearing as a secondary peak or shoulder distinct from the main intercalation peak complex; however, conventional methods for computing dQ/dV rely on finite differencing with filtering, which amplifies sensor noise and introduces bias in feature location. In this paper, we propose a Gaussian Process (GP) framework for lithium plating detection by directly modeling the charge-voltage relationship Q(V) as a stochastic process with calibrated uncertainty. Leveraging the property that derivatives of GPs remain GPs, we infer dQ/dV analytically and probabilistically from the posterior, enabling robust detection without ad hoc smoothing. The framework provides three key benefits: (i) noise-aware inference with hyperparameters learned from data, (ii) closed-form derivatives with credible intervals for uncertainty quantification, and (iii) scalability to online variants suitable for embedded BMS. Experimental validation on Li-ion coin cells across a range of C-rates (0.2C-1C) and temperatures (0-40$^\circ$C) demonstrates that the GP-based method reliably resolves distinct high-voltage secondary peak features under low-temperature, high-rate charging, while correctly reporting no features in non-plating cases. The concurrence of GP-identified differential features, reduced charge throughput, capacity fade measured via reference performance tests, and post-mortem microscopy confirmation supports the interpretation of these signatures as plating-related, establishing a practical pathway for real-time lithium plating detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26234 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Detection of Lithium Plating in Lithium-ion Cells: A Gaussian Process Approach Patnaik, Ayush Fogelquist, Jackson Zufall, Adam B Ji, Yiwei Robinson, Stephen K Bai, Peng Lin, Xinfan Machine Learning Systems and Control Lithium plating during fast charging is a critical degradation mechanism that accelerates capacity fade and can trigger catastrophic safety failures. Recent work has shown that plating onset can manifest in incremental-capacity analysis as an additional high-voltage feature above 4.0 V, often appearing as a secondary peak or shoulder distinct from the main intercalation peak complex; however, conventional methods for computing dQ/dV rely on finite differencing with filtering, which amplifies sensor noise and introduces bias in feature location. In this paper, we propose a Gaussian Process (GP) framework for lithium plating detection by directly modeling the charge-voltage relationship Q(V) as a stochastic process with calibrated uncertainty. Leveraging the property that derivatives of GPs remain GPs, we infer dQ/dV analytically and probabilistically from the posterior, enabling robust detection without ad hoc smoothing. The framework provides three key benefits: (i) noise-aware inference with hyperparameters learned from data, (ii) closed-form derivatives with credible intervals for uncertainty quantification, and (iii) scalability to online variants suitable for embedded BMS. Experimental validation on Li-ion coin cells across a range of C-rates (0.2C-1C) and temperatures (0-40$^\circ$C) demonstrates that the GP-based method reliably resolves distinct high-voltage secondary peak features under low-temperature, high-rate charging, while correctly reporting no features in non-plating cases. The concurrence of GP-identified differential features, reduced charge throughput, capacity fade measured via reference performance tests, and post-mortem microscopy confirmation supports the interpretation of these signatures as plating-related, establishing a practical pathway for real-time lithium plating detection. |
| title | Machine Learning Detection of Lithium Plating in Lithium-ion Cells: A Gaussian Process Approach |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2509.26234 |