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.

中文翻译：

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基于机器学习的镀锂潜力终身估计：实现健康感知型最快电池充电的途径


为了实现从化石燃料向可再生和可持续运输的转变，电池必须允许快速充电并延长使用寿命——这些目标在传统上是相互冲突的。当前的充电技术通常会损害一个属性而牺牲另一个属性，从而导致电池供电汽车带来不便或降低资源效率。对于锂离子电池，实现这两个目标的方法是使阳极表面的镀锂电位保持正值。在这项研究中，我们通过引入一种新方法来应对这一挑战，该方法涉及在锂离子电池的整个生命周期内实时监测和控制锂离子电池的电镀电位。我们在三电极电池上的实验结果表明，我们的方法可以使电池充电速度提高至少 30%，同时将其使用寿命几乎延长一倍。为了促进这些发现在商业应用中的采用，我们提出了一个基于机器学习的框架，利用电动汽车现成的电池数据进行终身电镀电位估计。所得模型在各种工作条件下表现出高保真度和稳健性，平均绝对误差仅为 3.37 mV。本研究概述了一种防止锂电镀并实现最快的健康电池充电的实用方法。