Study of Ion mobility modulation and AI-based electrode design for improved secondary battery performance

Abstract

While reducing ionic and electrical resistance is the primary method used to improve the electrochemical performance of secondary battery, selectively increasing resistance can also be used as a strategy to induce reversible reactions. Therefore, it is important to develop techniques to not only reduce resistance but to modulate it as needed. Unfortunately, increasing resistance appropriately and selectively is a challenge. Excessively high resistance can lead to a sharp increase in polarization, which can reduce the electrochemical performance of the secondary battery. Moreover, since ions migrate through the electrolyte filling the pores of the electrode and electrons are conducted through the solid components of the electrode, it is very difficult to selectively control the ionic and electrical resistance. To overcome these limitations, this paper proposes a method to determine the effect of increasing ionic resistance on the electrochemical performance of a secondary battery, control the resistance, and explore the balance between ionic and electrical resistance. The specific objectives are: (i) to introduce electrostatic shielding additives to generate resistance at the metal electrode protrusions to achieve a reversible reaction; (ii) to induce an internal field through the ferroelectric coating on the metal electrode surface to control the ionic conductivity; (iii) to utilize a deep learning model to predict the specific capacity of the lithium ion battery as a function of electrode design, and to explore the design of a cell with an target electrochemical performance.