A machine learning framework for early detection of lithium plating combining multiple physics-based electrochemical signatures

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.

Automated summary

The research established a framework for early detection of loss of Li inventory in lithium-ion batteries, distinguishing between Li plating and SEI-formation-dominated cells, using machine learning and multiple electrochemical signatures to improve classification accuracy.