Effect of cycling rate, particle size and transport properties on lithium-ion cathode performance

Much progress has been made in modeling the lithium-ion battery technology. There exists a critical need to establish a framework to assess the role of various physical, geometrical, and operating parameters and their relative influence on the energy and power capability of batteries. In this study, a surrogate modeling framework has been introduced to map the effect of design-related parameters on the performance of a lithium-ion cell. In particular, the effects of cycling rate, cathode particle size, and diffusion coefficient and electrical conductivity of the solid cathode material, on the specific energy and power have been studied using a cell-level model in conjunction with tools such as kriging, polynomial response, and radial-basis neural networks. Through global sensitivity analysis the relative impact of the various parameters are quantified under different scenarios. Specifically, the design space can be split into distinct regions based on the discharge and diffusion time scales for separate, more refined analysis. It is shown that the cathode performance becomes independent of the diffusion coefficient above a critical value. A Pareto-optimal front was constructed to quantify the tradeoff between maximum achievable energy and power levels. Such an analysis can provide guidelines for the optimization of the positive electrode design.