TUT14: Machine Learning and Artificial Intelligence in Batteries

THURSDAY, JULY 30 | 2:00 - 3:30 PM

ABOUT THIS TUTORIAL:

Machine learning (ML) promises to compress the time needed to characterize battery performance, lifetime and safety. By coupling ML with physical models and metrics, that learning can bridge across materials, chemistries and cell designs. This tutorial will discuss the most popular ML techniques and resources and review recent work in the electrochemical literature. Applications include materials discovery, image recognition for quantitative microscopy analysis, fast charge algorithm development and life prediction.

INSTRUCTOR:

Kandler Smith, PhD, Senior Engineer, Energy Storage, National Renewable Energy Laboratory

Kandler Smith (M’07) received the PhD in mechanical engineering from Pennsylvania State University, University Park, PA, USA, in electrochemical modeling and control of Li-ion batteries. He is a Senior Scientist and Principal Investigator with the Transportation and Hydrogen Systems Center, Energy Storage Group at the U.S. Department of Energy's National Renewable Energy Laboratory, Golden, CO, USA. He recently coauthored a textbook on design and analysis of large Li-ion battery systems. His research interests include Li-ion battery lifetime prediction, multiphysics modeling, and computational design.

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