Machine learning-based lifelong estimation of lithium plating potential: A path to health-aware fastest battery charging

Artikel i vetenskaplig tidskrift, 2024

To enable a shift from fossil fuels to renewable and sustainable transport, batteries must allow fast charging and exhibit extended lifetimes---objectives that traditionally conflict. Current charging technologies often compromise one attribute for the other, leading to either inconvenience or diminished resource efficiency in battery-powered vehicles. For lithium-ion batteries, the way to meet both objectives is for the lithium plating potential at the anode surface to remain positive. In this study, we address this challenge by introducing a novel method that involves real-time monitoring and control of the plating potential in lithium-ion battery cells throughout their lifespan. Our experimental results on three-electrode cells reveal that our approach can enable batteries to charge at least 30% faster while almost doubling their lifetime. To facilitate the adoption of these findings in commercial applications, we propose a machine learning-based framework for lifelong plating potential estimation, utilizing readily available battery data from electric vehicles. The resulting model demonstrates high fidelity and robustness under diverse operating conditions, achieving a mean absolute error of merely 3.37 mV. This research outlines a practical methodology to prevent lithium plating and enable the fastest health-conscious battery charging.