State-of-health estimation using a neural network trained on vehicle data

Jacob C. Hamar, Simon V. Erhard, Angelo Canesso, Jonas Kohlschmidt, Nicolas Olivain, Andreas Jossen

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

The validation of battery aging models in automotive applications requires reliable aging data to compare the accuracy of each proposed model. Using a sample of 704 vehicles aged up to eight years under diverse nominal conditions two aging estimation models are proposed. By analyzing relevant automobile battery data a more relevant fit of a semi-empirical holistic model is provided with an Arrhenius temperature dependence and pseudo-Tafel voltage dependence. As a comparison, a neural network capturing the aging behavior using the most correlated variables available in the data-set was also developed. Over 110,000 measurements from seven relevant indicators are available as aging predictors, as well as, highly-accurate capacity measurements which is used as the ground truth capacity targets to train and validate the proposed models. Against these points the Semi-Empirical and Neural Network models achieved a root mean squared error of 3.4%-SOH and 3.0%-SOH, respectively.

Original languageEnglish
Article number230493
JournalJournal of Power Sources
Volume512
DOIs
StatePublished - 15 Nov 2021

Keywords

  • Aging
  • Automotive
  • Big data
  • Machine learning

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