Optimization of Electrode Thickness of Lithium-Ion Battery Cell for Maximizing Energy Density

Abstract

The demand for high capacity and high energy density Lithium-ion batteries (LIBs) has drastically increased nowadays. One way of meeting that rising demand is to design LIBs with thicker electrodes. Increasing electrode thickness can enhance the energy density of LIB cells by reducing the ratio of inactive materials in the cell. However, after a certain value of electrode thickness, the rate of energy density increase becomes slower. On the other hand, the impact of associated limitations becomes stronger, reducing the practical applicability of LIBs with thicker electrodes. Hence, an optimum value of thickness is of utmost importance for the practicability of thicker electrode design. In this paper, both cathode thickness and anode thickness of an NCM LIB cell were optimized by applying response surface methodology (RSM) with a Box-Behnken design (BBD) to maximize the energy density. Moreover, the influence of electrode porosity, together with the interaction of porosity with cathode and anode thickness, was incorporated into the optimization. A full factorial design of 3-level, 3-factor was used to generate 15 simulation conditions in accordance with the design of experiment (DoE) achieved through BBD. Then those conditions were used to achieve 15 responses by simulating reduced-order electrochemical model. Finally, statistical technique analysis of variance (ANOVA) was used to analyze and validate the results of RSM. The results show that the RSM-BBD optimization method, coupled with ANOVA analysis, has successfully optimized the thicknesses of both positive and negative electrodes for maximum energy density, despite the nonlinearity of the electrochemical system. The findings suggest an optimized cathode thickness of 401.56 μm and anode thickness of 186.36 μm for maximum energy density of 292.22 of an NCM LIB cell, while electrode porosity is preferred to be 0.2.

Keywords: energy density, electrode thickness, optimum values, response surface methodology, analysis of variance, design of experiments