Bayesian calibration for electrochemical thermal model of lithium-ion cells

• Published in: Journal of power sources 2016-07, Vol.320, p.296-309
• Main Authors: Tagade, Piyush, Hariharan, Krishnan S., Basu, Suman, Verma, Mohan Kumar Singh, Kolake, Subramanya Mayya, Song, Taewon, Oh, Dukjin, Yeo, Taejung, Doo, Seokgwang
• Format: Article
• Language: English
• Subjects: Bayesian analysis; Bayesian calibration; Bonding; Calibration; Electric cells; Electrochemical thermal model; Lithium; Lithium batteries; Lithium-ion batteries; Markov chain monte carlo; Mathematical models; Parameter estimation; Plating
• ISSN: 0378-7753; 1873-2755
• DOI: 10.1016/j.jpowsour.2016.04.106

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. •Bayesian framework for calibration of the P2D-ECT model.•Matrix-variate Gaussian process for computationally efficient implementation.•P2D-ECT model parameter estimation and quantification of model uncertainty.•Accurate model prediction across a range of temperatures.•Novel insights into low temperature Li-ion cell operation.