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

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.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This study presents a data-driven numerical method for evaluating the immersion cooling behavior of Lithium-ion battery packs. Experimental validation and model calibration are performed, and the learned distributions and numerical model are used to design a system for realistic racing car operation, with further sensitivity analysis.