A key challenge for lithium (Li)-ion batteries is the capability to manage battery performance and predict lifetime. Early detection of battery-aging phenomena and the implications for the performance are crucial for maintaining warranty and avoiding safety-related liabilities. We established a framework for early detection of loss of Li inventory, which is further separated into Li plating and normal solid electrolyte interphase (SEI) formation. Although SEI formation is inevitable, Li plating causes serious degradation and safety issues. Therefore, Li plating must be identified and avoided. Our framework differentiates Li plating from SEI-formation-dominated cells based on data from the first 25 aging cycles. This classification framework is based on machine learning (ML); multiple coherent and physically meaningful electrochemical signatures along the aging process are used. We also demonstrate that multiple electrochemical signatures must be combined to increase accuracy in the classification.

锂（Li）离子电池的主要挑战是管理电池性能和预测寿命的能力。尽早发现电池老化现象及其对性能的影响对于维持保修和避免安全相关的责任至关重要。我们建立了一个早期检测Li存量损失的框架，该框架进一步分为Li镀层和正常的固体电解质中间相（SEI）形成。尽管不可避免地会形成SEI，但Li镀层会导致严重的降解和安全问题。因此，必须识别和避免镀锂。我们的框架基于前25个老化周期的数据，将Li镀层与SEI形成为主的电池区分开来。该分类框架基于机器学习（ML）；在老化过程中使用了多个连贯且具有物理意义的电化学特征。我们还证明了必须将多个电化学特征结合起来以提高分类的准确性。