Predicting Battery Capacity from Impedance at Varying Temperature and State of Charge Using Machine Learning

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

Research output: Contribution to journalArticlepeer-review

17 Scopus Citations

Abstract

Prediction of battery health from electrochemical impedance spectroscopy (EIS) data can enable rapid measurement of battery state in real-world applications without using additional sensors or time-consuming performance measurements. However, deconvoluting the effect of capacity, state of charge, and temperature on EIS response is complicated analytically. Here, various machine-learning models, such as linear, Gaussian process, random forest, and artificial neural network regression, are utilized to predict capacity from EIS using hundreds of capacity, direct current (DC) resistance, and EIS measurements recorded under varying conditions of health, temperature, and state of charge (SOC). Several feature extraction and selection methods from traditional electrochemical analysis and statistical modeling are explored using machine-learning pipelines. EIS data from just two frequencies can accurately predict capacity, and interrogation shows that the optimal set of frequencies is not usually intuitive. Best results are achieved with an ensemble model, which predicts battery capacity with a mean absolute error of 1.9% on data from unobserved cells.

Original languageAmerican English
Article number101184
Number of pages24
JournalCell Reports Physical Science
Volume3
Issue number12
DOIs
StatePublished - 21 Dec 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors

NREL Publication Number

  • NREL/JA-5700-82892

Keywords

  • battery health
  • degradation
  • electrochemical impedance spectroscopy
  • feature extraction
  • feature selection
  • lifetime
  • lithium-ion battery
  • machine learning

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