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

• Published in: Energy (Oxford) Vol. 249; p. 123633
• Main Authors: Solai, Elie, Guadagnini, Maxime, Beaugendre, Héloïse, Daccord, Rémi, Congedo, Pietro
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.06.2022; Elsevier BV; Elsevier
• Subjects: Batteries; Bayesian analysis; Bayesian calibration; Calibration; Cooling; Electric vehicles; Experimental dataset; Fluid Dynamics; Heat transfer; Immersion; Immersion cooling; Lithium; Lithium-ion batteries; Mathematical models; Numerical methods; Numerical models; Numerical simulation; Physics; Race cars; Rechargeable batteries; Sensitivity analysis; Surrogate model; Thermal management; Thermodynamic properties; Uncertainty quantification; Lithium-ion batteries; Bayesian calibration; Sensitivity analysis; Surrogate model; Immersion cooling; Uncertainty quantification; Numerical simulation; Experimental dataset
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2022.123633

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. •We develop a data-driven fast numerical model to predict the behavior of the battery pack.•We provide data of an experimental test case of Li-ion cells immersion cooling.•We apply Bayesian calibration method to get posterior distributions of input parameters using experimental data.•Our approach allows reducing the numerical error bar of the model temperature response dramatically.