State of charge and state of health estimation using electrochemical acoustic time of flight analysis

Greg Davies, Kevin W. Knehr, Barry Van Tassell, Thomas Hodson, Shaurjo Biswas, Andrew G. Hsieh, Daniel Artemus Steingart

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Ultrasonic analysis was used to predict the state of charge and state of health of lithium-ion pouch cells that have been cycled for several hundred cycles. The repeatable ultrasonic trends are reduced to two key metrics: time of flight shift and total signal amplitude, which are then used with voltage data in a supervised machine learning technique to build a model for state of charge (SOC) prediction. Using this model, cell SOC is predicted to ∼1% accuracy for both lithium cobalt oxide and lithium iron phosphate cells. Elastic wave propagation theory is used to explain that the changes in ultrasonic signal are related to changes in the material properties of the active materials (i.e., elastic modulus and density) during cycling. Finally, we show the machine learning model can accurately predict cell state of health with an error ∼1%. This is accomplished by extending the data inputs into the model to include full ultrasonic waveforms at top of charge.

Original languageEnglish (US)
Pages (from-to)A2746-A2755
JournalJournal of the Electrochemical Society
Volume164
Issue number12
DOIs
StatePublished - 2017

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Renewable Energy, Sustainability and the Environment
  • Surfaces, Coatings and Films
  • Electrochemistry
  • Materials Chemistry

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