Validation of a data-driven fast numerical model to simulate the immersion cooling of a lithium-ion battery pack

Elie Solai, Maxime Guadagnini, Héloïse Beaugendre, Rémi Daccord and Pietro Congedo

Energy, 2022, vol. 249, issue C

Abstract: Thermal management of Lithium-ion batteries is a key element to the widespread of electric vehicles. In this study, we illustrate the validation of a data-driven numerical method permitting to evaluate fast the behavior of the Immersion Cooling of a Lithium-ion Battery Pack. First, we illustrate an experiment using a set up of immersion cooling battery pack, where the temperatures, voltage and electrical current evolution of the Li-ion batteries are monitored. The impact of different charging/discharging cycles on the thermal behavior of the battery pack is investigated. Secondly, we introduce a numerical model, that simulates the heat transfer and electrical behavior of an immersion cooling Battery Thermal Management System. The deterministic numerical model is compared against the experimental measurements of temperatures. Then, we perform a Bayesian calibration of the multi-physics input parameters using the experimental measurements directly. The informative distributions outcoming of this process are used to validate the model in different experimental conditions and reduce the uncertainty in the model's temperatures predictions. Finally, the learned distributions of inputs and the numerical model are used to design the system under realistic conditions representing a realistic racing car operation. A Sobol indices based sensitivity analysis is performed to get further analysis elements on the behavior of the BTMS.

Keywords: Lithium-ion batteries; Immersion cooling; Experimental dataset; Numerical simulation; Uncertainty quantification; Sensitivity analysis; Surrogate model; Bayesian calibration (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:249:y:2022:i:c:s0360544222005369

DOI: 10.1016/j.energy.2022.123633

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