A Framework to Optimize Electrode Morphology

Abstract

Since the invention of lithium-ion batteries, porous electrodes have been manufactured in essentially the same way: granulating active materials into powder, mixing them with some additives such as carbon black and binder, and pasting them on conductive substrate. Optimizations have been focusing on tuning parameters such as particle sizes, porosity and electrode thickness, with the assumption of a uniform electrode. Some recent work demonstrated better cell performance by engineering the shape of the electrode with features such as nano cylinders, tunnels and fractal branches. 3D printing has been used to fabricate the structure of batter electrode. On the simulation side, however, there have been few reports on freeform electrode shape optimization. Most shape optimizations assume a particular electrode pattern with a few adjustable dimensional parameters. Here we report a method to optimize porosity distribution of electrode without a prior assumption of the pattern. An NMC 333 electrode with lithium metal as the counter electrode is considered as an example. To find the optimal solid material distribution for maximum specific energy, we represented the NMC material distribution by discrete nodal design variables. To solve this high dimensional optimization problem, we used a gradient-free optimization algorithm, self-directed online learning optimization. We show that the method can produce an electrode pattern with the specific energy more than 15% higher than that of the uniform electrode. This generic distribution optimization approach provides a powerful tool for the design and optimization of porous electrodes. Figure 1