Numerical investigation on the thermal management of lithium-ion battery system and cooling effect optimization

The widespread application of lithium-ion batteries as the practice facility of energy storage has come alongside much unforeseen fire safety and thermal runaway issues that leads to increasing research interests. A compre-hensive understanding of the thermal features of battery packs and the heat exchange process of energy storage systems is imperative. In this paper, a three-dimensional thermo-electrochemical model has been developed to simulate the detailed temperature distribution of battery packs. The numerical analysis of the cooling effect with both natural and forced air ventilation configurations are compared as well. Moreover, the artificial neural network (ANN) model was coupled with the computational fluid dynamics (CFD) simulation results to perform an optimization of a specific configuration battery system considering configuration dimensions and operating conditions simultaneously. The ANN model builds a relationship between battery spacing and ambient cooling properties. It was found that the changing of ambient pressure creates a larger temperature drop under the forced air cooling than that under natural ventilation. The optimum design for the battery pack can decrease the maximum temperature and the temperature difference by 1.94% and 17%, respectively. Overall, the present modelling framework presents an innovative approach to utilising high-fidelity CFD numerical results as inputs for establishing ANN training dataset, potentially enhancing the state-of-art thermal management of lithium-ion battery systems reducing the risks of thermal runaway and fire outbreak.

Automated summary

In this study, a three-dimensional thermo-electrochemical model was developed to simulate the temperature distribution of battery packs. The cooling effect of natural and forced air ventilation configurations was compared, and it was found that forced air cooling had a greater impact on temperature reduction. The artificial neural network was coupled with computational fluid dynamics simulation results to optimize specific battery system configurations.