Novel Method for Evaluation and Prediction of Capacity Loss Metrics in Li-Ion Electrochemical Cells

Practical methods and metrics are needed to assist battery development and end-user communities in the area of battery aging, in particular, understanding capacity loss in Li-ion cells. Tools are sought that offer both diagnostic and prognostic benefits, while minimizing the need for prolonged testing or undue commitment of tangible resources. Based on a chemical engineering batch reactor approach to cell aging, this work is a move in the direction to meet such needs. Capacity loss is interpreted by a combination of sigmoidal rate expressions, having physically-meaningful parameters, which cover chief mechanisms that affect loss of available lithium and loss of active host material. A lithium source term is also accommodated by the modeling approach. Development is shown to identify reversible and irreversible capacity loss contributions, as well as calculate molar-based terms for lithium and active sites, and how these change over time due to cell aging. The method is demonstrated on NCA/graphite cell chemistries, where conditions of cycle-life, calendar-life, and temperature are considered. The resultant capability adds value toward deepening our understanding of aging contributions that impact capacity, and provides a foundation for improving Li-ion cell design and management through diagnostic and predictive elements.

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