Accuracy assessment of an internal resistance model of Li-ion batteries in immersion cooling configuration
SOLAI, Elie
Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
BEAUGENDRE, Heloise
Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
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Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
SOLAI, Elie
Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
BEAUGENDRE, Heloise
Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
< Réduire
Certified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
Langue
en
Article de revue
Ce document a été publié dans
Applied Thermal Engineering. 2023-02-05, vol. 220, n° 119656
Elsevier
Résumé en anglais
Internal resistance is a critical parameter of the thermal behavior of Li-ion battery cells. This paper proposes an innovative way to deal with the uncertainties related to this physical parameter using experimental data ...Lire la suite >
Internal resistance is a critical parameter of the thermal behavior of Li-ion battery cells. This paper proposes an innovative way to deal with the uncertainties related to this physical parameter using experimental data and numerical simulation. First, a CFD model is validated against an experimental configuration representing the behavior of heated Li-ion battery cells under constant discharging current conditions. Secondly, an Uncertainty Quantification based methodology is proposed to represent the internal resistance and its inherent uncertainties. Thanks to an accurate and fast to compute surrogate model, the impact of those uncertainties on the temperature evolution of Li-ion cells is quantified. Finally, Bayesian inference of the internal resistance model parameters using experimental measurements is performed, reducing the prediction uncertainty by almost 95% for some temperatures of interest. Finally, an enhanced internal model is constructed by considering the state of charge and temperature dependency on internal resistance. This model is implemented in the CFD code and used to model a full discharge of the Li-ion batteries. The resulting temperature evolution computed with the two different resistance models is compared for the low state of charge situations.< Réduire
Mots clés en anglais
Lithium-ion batteries
Numerical simulation
Uncertainty quantification
Surrogate model
Kriging
Bayesian calibration
Immersion cooling
Origine
Importé de halUnités de recherche