报告时间：5月11日周四上午9:30
报告地点：机械楼227会议室
报告题目：基于可解释性数据科学技术的电池智能制造
报告人：刘凯龙教授（山东大学，国家海外高层次人才）
【报告人简介】
刘凯龙，主要从事交通电气化、储能系统建模、管理与控制研究。入选2022年国家海外高层次人才项目，以第一或通讯作者发表SCI一区学术论文40余篇，撰写学术专著3部，授权国内外发明专利5项，获评IEEE Trans. on Industrial Electronics、Springer、LSMS2021等国际期刊与会议最佳论文5项，获英国未来领袖基金提名，成果成功应用于Varta Storage、阿斯顿马丁、UKBIC等多家知名企业，担任IEEE Trans. on Transportation Electrification等国际期刊常驻编委等。
【报告摘要】
Lithium-ion batteries have become one of the most promising sources for accelerating the development of transportation electrification, where effective electrode manufacturing plays a key role in determining battery performance. Due to the highly complicated process and strongly coupled interdependencies of electrode production, a data-science solution that can analysis feature variables within manufacturing chain and achieve reliable prediction is urgently needed. This work proposes two feasible data-science solutions through using interpretable machine learning techniques, for effectively quantifying the importance and correlations of battery manufacturing features and their effects on the predictions of electrode properties. Illustrative results demonstrate that the proposed data-science frameworks not only achieve the reliable predictions of electrode properties but also leads to the effective quantification of both manufacturing feature importance and correlations. This is the first time to design the systematic data-driven frameworks for directly quantifying battery production feature importance and correlations by various evaluation criteria, paving a promising solution to reduce model dimension and conduct efficient sensitivity analysis of battery manufacturing.
