Tutorial: Machine Learning and Artificial Intelligence in Batteries

Kandler Smith, Paul Gasper, Andrew Schiek, Francois Usseglio-Viretta, Eric Dufek, Ross Kunz, Kevin Gering

Research output: NRELPresentation


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.
Original languageAmerican English
Number of pages98
StatePublished - 2020

Publication series

NamePresented at the 2020 International Battery Seminar & Exhibit, 28-30 July 2020

NREL Publication Number

  • NREL/PR-5700-78367


  • artificial intelligence
  • battery
  • design
  • image recognition
  • life prediction
  • lithium-ion
  • machine learning
  • microscopy
  • neural network
  • testing


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