Physically-based reduced-order capacity loss model for graphite anodes in Li-ion battery cells

Physically-based Li-ion electrochemical cell models have been shown capable of predicting cell performance and degradation, but are computationally expensive for optimization-oriented design applications. Faster empirical models have been developed from experimental data, but are not generalizable to operating conditions outside of the range established by the calibration data. In this paper, a reduced-order capacity-loss model for graphite anodes is derived based upon the salient physical loss mechanisms to improve computational efficiency without sacrificing model fidelity. This model captures the two primary degradation mechanisms that occur in the graphite anode of a typical lithium ion cell: a) capacity loss due to Solid Electrolyte Interface (SEI) layer growth, and b) capacity loss due to isolation of active material. The model is calibrated and validated for a commercial 2.3-Ah cell with a Lithium Iron Phosphate (LFP) cathode and graphite anode. One data set is used for calibration, another four experimental data sets are used for validation. The model matches experimental capacity degradation results within a 20% error. Moreover, the reported model is 2400× faster than currently existing more complex physically-based electrochemical models that are only slightly more accurate (in some cases).