Bayesian calibration for electrochemical thermal model of lithium-ion cells

Bayesian calibration for electrochemical thermal model of lithium-ion cells Pseudo-two dimensional electrochemical thermal (P2D-ECT) model contains many parameters that are difficult to evaluate experimentally. Estimation of these model parameters is challenging due to computational cost and the transient model. Due to lack of complete physical understanding, this issue gets aggravated at extreme conditions like low temperature (LT) operations. This paper presents a Bayesian calibration framework for estimation of the P2D-ECT model parameters. The framework uses a matrix variate Gaussian process representation to obtain a computationally tractable formulation for calibration of the transient model. Performance of the framework is investigated for calibration of the P2D-ECT model across a range of temperatures (333 K263 K) and operating protocols. In the absence of complete physical understanding, the framework also quantifies structural uncertainty in the calibrated model. This information is used by the framework to test validity of the new physical phenomena before incorporation in the model. This capability is demonstrated by introducing temperature dependence on Bruggeman's coefficient and lithium plating formation at LT. With the incorporation of new physics, the calibrated P2D-ECT model accurately predicts the cell voltage with high confidence. The accurate predictions are used to obtain new insights into the low temperature lithium ion cell behavior. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Power Sources Elsevier

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier B.V.
ISSN
0378-7753
D.O.I.
10.1016/j.jpowsour.2016.04.106
Publisher site
See Article on Publisher Site

Abstract

Pseudo-two dimensional electrochemical thermal (P2D-ECT) model contains many parameters that are difficult to evaluate experimentally. Estimation of these model parameters is challenging due to computational cost and the transient model. Due to lack of complete physical understanding, this issue gets aggravated at extreme conditions like low temperature (LT) operations. This paper presents a Bayesian calibration framework for estimation of the P2D-ECT model parameters. The framework uses a matrix variate Gaussian process representation to obtain a computationally tractable formulation for calibration of the transient model. Performance of the framework is investigated for calibration of the P2D-ECT model across a range of temperatures (333 K263 K) and operating protocols. In the absence of complete physical understanding, the framework also quantifies structural uncertainty in the calibrated model. This information is used by the framework to test validity of the new physical phenomena before incorporation in the model. This capability is demonstrated by introducing temperature dependence on Bruggeman's coefficient and lithium plating formation at LT. With the incorporation of new physics, the calibrated P2D-ECT model accurately predicts the cell voltage with high confidence. The accurate predictions are used to obtain new insights into the low temperature lithium ion cell behavior.

Journal

Journal of Power SourcesElsevier

Published: Jul 15, 2016

References

  • J. Power Sources
    Johnson, V.
  • Li-ion battery parameter estimation for state of charge
    Tang, X.; Mao, X.; Lin, J.; Koch, B.
  • Adaptive parameter identification and state-of-charge estimation of lithium-ion batteries
    Eichi, H.; Chow, M.
  • Phys. Rev. Lett.
    Parthasarathy, R.; Lin, X.-M.; Elteto, K.; Rosenbaum, T.F.; Jaeger, H.

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