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

Author:

Gao Tianhan,Lu WeiORCID

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.

Funder

Division of Computer and Network Systems

Publisher

The Electrochemical Society

Subject

Materials Chemistry,Electrochemistry,Surfaces, Coatings and Films,Condensed Matter Physics,Renewable Energy, Sustainability and the Environment,Electronic, Optical and Magnetic Materials

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3