A Quantitative Analytical Model for Predicting and Optimizing the Rate Performance of Battery Cells

Abstract

The prediction of the performance of battery cells is usually accomplished by computationally expensive numerical simulations. Here, we present a simple analytical model as an efficient alternative to predict the rate capability of battery cells limited by electrolyte transport without the need to fit parameters to simulations. It exhibits very good agreement with simulations over a wide range of discharge rate and electrode thickness and offers a speedup of >10 5 times. The optimal electrode properties predicted by the model differ <∼10% from simulation results, suggesting it as an attractive computational tool for the cell-level design of batteries. The model reveals that the discharge capacities of half- and full cells exhibit qualitatively different scaling relations with electrode thickness and current density, and the rate performance of thick electrodes can be improved by avoiding electrode materials (e.g., LiFePO 4 , Li 4 Ti 5 O 12 ) whose open-circuit potentials are insensitive to the state of charge. A simple analytical model to predict battery cell rate performance The model can replace P2D battery simulations with >100,000-fold speedup Performance of half- versus full battery cells scales differently with cell properties A prediction that NMC has better rate capability than LiFePO 4 in thick electrodes Prediction of battery cell performance is traditionally accomplished by sophisticated numerical simulations. Wang and Tang develop a simple analytical model implemented in open-source code as an efficient alternative, which enables quantitative design and optimization at a negligible computational cost and offers revealing insights into the dependence of rate capability on cell properties.