Early Battery Performance Prediction for Mixed Use Charging Profiles Using Hierarchal Machine Learning

M. Ross Kunz, Eric J. Dufek, Zonggen Yi, Kevin L. Gering, Matthew G. Shirk, Kandler Smith, Bor Rong Chen, Qiang Wang, Paul Gasper, Randy L. Bewley, Tanvir R. Tanim

Research output: Contribution to journalArticlepeer-review

10 Scopus Citations


A key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, the validation process can be accelerated. However, questions on the validity of projecting models trained on limited data or simple cycling profiles, such as constant current cycling, to real-world scenarios with complex loads remains. Here, we present the ability to predict performance with less than 1.2 % mean absolute percent error when trained on cells aged using complex electric vehicle discharge profiles, and either AC Level 2 charge or DC Fast charge profiles, using only the first 45 cycles, namely 5 % of the total testing time. While error is low across the projections, this study also highlights that battery lifetime analysis using only cycling data may not extrapolate safely to certain real-world conditions due to the impact of calendar degradation.

Original languageAmerican English
Pages (from-to)1186-1196
Number of pages11
JournalBatteries and Supercaps
Issue number7
StatePublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 Wiley-VCH GmbH.

NREL Publication Number

  • NREL/JA-5700-80471


  • Battery performance prediction
  • Calendar aging
  • Cycle life
  • Elastic Net
  • Machine learning


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