Physical Model and Machine Learning Enabled Electrolyte Channel Design for Fast Charging

Abstract

Thick electrode is highly effective to increase the specific energy of a battery cell, but the associated increase in transport distance causes a major barrier for fast charging. We introduce a bio-inspired electrolyte channel design into thick electrodes to improve the cell performance, especially under fast charging conditions. The effects of channel length, width, tapering degree and active material width on the electrochemical performance and mechanical integrity are investigated. Machine learning by deep neural network (DNN) is developed to relate the geometrical parameters of channels to the overall cell performance. Integrating machine learning with the Markov chain Monte Carlo gradient descent optimization, we demonstrate that the complicated multivariable channel geometry optimization problem can be efficiently solved. The results show that within a certain range of geometrical parameters, the specific energy, specific capacity and specific power can be greatly improved. At the same time, the maximum first principal stress which is in the cathode region next to the separator can be significantly reduced, giving better mechanical integrity. Comparing to conventional-designed cells without electrolyte channels, we show a 79% increase in specific energy using channel design optimization. This study provides a design strategy and optimization method to achieve significantly improved battery performance.