Machine-Learning for Battery Health Diagnosis

Paul Gasper, Andrew Schiek, Kandler Smith, Shuhei Yoshida, Yuta Shimonishi

Research output: NRELPresentation


Battery health diagnosis often requires time-consuming measurements in laboratory conditions, but these types of measurements are unsuitable for use in real-world application such as electric vehicles. Instead, rapid measurements that can be conducted at varying environmental conditions need to be used to monitor battery health. Machine-learning techniques can then be utilized to connect these rapid measurements to health diagnostic information recorded in the lab. This presentation is a part of a tutorial on using machine-learning methods to analyze and predict battery state.
Original languageAmerican English
Number of pages42
StatePublished - 2022

Publication series

NamePresented at the MRS Spring Meeting, 8-13 May 2022, Honolulu, Hawaii

NREL Publication Number

  • NREL/PR-5700-82551


  • battery
  • battery health
  • degradation
  • electrochemical impedance spectroscopy
  • machine-learning


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