Dataset of Rapid Prediction of Maximal Temperature in Liquid-Cooled Li-Ion Battery Modules: A Case Study Using Polymeric Hollow Fiber Cooling Channels

The growing demand for high-performance electric vehicles underscores the need for efficient thermal management systems for Li-ion batteries. Rapid and precise numerical models are essential, as they offer cost-effective and time-efficient solutions. This study presents a novel approach for predicting the maximal temperature in cylindrical Li-ion batteries cooled with a polymeric hollow fiber heat exchanger. Experimental measurements revealed that the maximal temperature during constant-current discharging can be expressed as an exponential function of the coolant flow rate. Leveraging this insight, a simplified computational model was developed to estimate the maximal temperature of the system. The model relies on two extreme coolant flow conditions: (1) zero flow and (2) very high flow, where heat transfer is limited only by conduction through the solid wall of the cooling fibers.This dataset includes data from the experimental temperature measurements during the discharging of a battery pack compared to numerical simulation data.The necessary user-defined-function (UDF) code written in C, which was implemented in ANSYS FLUENT for source term is presented in the dataset.