Battery Electrode Design Process Using Deep Neural Network

Abstract

Abstract The transition to electric vehicles (EVs) demands lithium-ion batteries (LIBs) customized for diverse energy and power density requirements, yet the intricate trade-offs among electrode design parameters make conventional trial-and-error optimization costly and time-consuming. This study presents the battery electrode exploration network (BattleNet), a deep learning model that predicts the specific capacity across multiple C-rates, collectively representing the rate capability, from electrode design parameters, including loading level, thickness, and porosity. Trained entirely on real-world experimental data, BattleNet achieved an R2 of 0.889, outperforming conventional machine learning baselines. Based on these predictions, an inverse design framework was proposed to explore design parameters satisfying a target rate capability. The explored parameters matched the target with an average error of 7.9%, and cells fabricated within the explored region satisfied the target performance with an average error of merely 4.5%. These results demonstrate the practical feasibility of deep learning as a tool to accelerate LIB design and development for the EV industry.