# Machine Learning Detection of Lithium Plating in Lithium-ion Cells: A Gaussian Process Approach Source: [Machine Learning Detection of Lithium Plating in Lithium-ion Cells: A Gaussian Process Approach]() Collection index: [llms.txt]() Data updated: 2026-03-18T05:00:04.466Z Authors: Ayush Patnaik, Jackson Fogelquist, Adam B Zufall, Yiwei Ji, Stephen K Robinson, Peng Bai, Xinfan Lin Published: 2025-09-30T13:30:06.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Lithium plating detection and degradation analysis on EEMB LIR-2032 graphite\|\|LiCoO2 coin cells](): Lithium plating detection and degradation analysis; EEMB LIR-2032 graphite\|\|LiCoO2 coin cells; Charge Throughput Decrease Rate: 0.029. ## Original sources and implementation [Original paper]() [PDF]()