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

TL;DR

This paper introduces a simple, parameter-free analytical model that accurately predicts and optimizes the rate performance of lithium-ion battery cells, significantly speeding up design processes and providing practical insights for electrode improvement.

Contribution

The authors develop a novel analytical model that predicts battery rate performance without fitting parameters, matching simulation results and enabling faster cell design.

Findings

Model predicts rate performance with less than 10% error compared to simulations.

Achieves over 10^5 times speedup in computation.

Provides practical strategies for improving thick electrode performance.

Abstract

An important objective of designing lithium-ion rechargeable battery cells is to maximize their rate performance without compromising the energy density, which is mainly achieved through computationally expensive numerical simulations at present. Here we present a simple analytical model for predicting the rate performance of battery cells limited by electrolyte transport without any fitting parameters. 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 are of less than 10% difference from simulation results, suggesting it as an attractive computational tool for the cell-level battery architecture design. The model also offers important insights on practical ways to improve the rate performance of thick electrodes, including avoiding electrode materials such as LiFePO$_4$ and Li$_4$Ti$_5$O$_{12}$ whose open-circuit potentials are insensitive to the state of charge and utilizing lithium metal anode to synergistically accelerate electrolyte transport within thick cathodes.