Machine Learning Detection of Lithium Plating in Lithium-ion Cells: A Gaussian Process Approach

2025-09-30

AI Summary

Lithium plating during fast charging of lithium-ion batteries accelerates capacity fade and poses safety risks. Conventional detection methods based on dQ/dV curve analysis—particularly the characteristic peak above 4.0 V—are susceptible to noise amplification induced by finite-difference approximation and filtering, leading to inaccurate peak localization. This work proposes a Gaussian process regression (GPR)-based machine learning framework that directly models the charge–voltage relationship Q(V). Leveraging the analytical property that the derivative of a Gaussian process remains Gaussian, the method enables exact, noise-robust inference of dQ/dV and rigorous uncertainty quantification—without requiring heuristic smoothing or filtering. The approach supports noise-aware estimation, probabilistic confidence intervals, and embedded online deployment. Experimental validation across multiple temperatures and C-rates demonstrates robust lithium-plating peak identification, zero false positives under baseline conditions, and strong correlation with measured capacity loss and coulombic inefficiency.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Learning & Optimization for CV
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Lithium plating during fast charging is a critical degradation mechanism that accelerates capacity fade and can trigger catastrophic safety failures. Recent work has identified a distinctive dQ/dV peak above 4.0 V as a reliable signature of plating onset; however, conventional methods for computing dQ/dV rely on finite differencing with filtering, which amplifies sensor noise and introduces bias in peak 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°C) demonstrates that the GP-based method reliably detects plating peaks under low-temperature, high-rate charging, while correctly reporting no peaks in baseline cases. The concurrence of GP-identified differential peaks, reduced charge throughput, and capacity fade measured via reference performance tests confirms the method's accuracy and robustness, establishing a practical pathway for real-time lithium plating detection.

Problem

• Detects lithium plating in batteries using Gaussian Process modeling
• Addresses noise amplification in conventional dQ/dV peak detection methods
• Enables real-time plating detection with uncertainty quantification

Innovation

• Gaussian Process models charge-voltage relationship directly
• Analytically infers dQ/dV derivatives with uncertainty quantification
• Provides scalable online detection for battery management systems