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In the dynamic realm of energy storage, lithium-ion batteries stand out as a frontrunner, powering a myriad of devices from smartphones to electric vehicles. However, efficient heat management is crucial for ensuring the longevity and safety of these batteries. This paper aims to delve into the process of lithium-ion battery heat management systems, exploring how cutting-edge technologies are used to regulate temperature and optimize performance. In addition, computational fluid dynamics (CFD) studies take center stage, offering insights into the intricate thermal dynamics within these powerhouses.

In this study, thermal behavior of pouch type lithium-ion battery cell has been investigated by using CFD method. Result of different discharge rates have been evaluated by using multi-scale multi-dimensional (MSMD) battery model. By using MSMD Model 0.5C, 1C, 2C, 3C and 5C discharge rates are compared in equivalent circuit model (ECM) and NTGK empirical models by monitoring averaged surface temperature on battery body wall. In addition, on NTGK model, air cooling effect has been studied with the 0.1 m/s, 0.2 m/s and 0.5 m/s air, velocities.

Results shows that higher discharge rate causes higher temperature on battery zones and air cooling is effective to obtain the lower zone temperatures. Also, ECM model gives higher temperature than NTGK model on battery zone.

When the literature is evaluated, comparison of the models used in battery cooling (ECM and NTGK) has never been done before. Within the scope of this study, model comparison was made. At the same time, the time step has always been ignored in the literature. In this study, both time step and forced convection conditions were considered when comparing the models.

In this study, it is aimed to develop cooling models for the efficient use of batteries and to show how important the busbar material is. Batteries, which are indispensable energy sources of electric aircraft, automobiles and portable devices, may eventually run out. Battery life decreases over time; the most critical factor is temperature. The temperature of batteries should not exceed the safe operating temperature of 313 K and it is recommended to have a balanced temperature distribution through the battery.

In this study, the effect on the battery temperature caused by using different busbar materials to connect batteries together was investigated. Gold, copper and titanium were chosen as the different busbar material. The Air velocities used were 1 m/s and 2 m/s, the air inlet temperatures were 295 and 300 K and the discharge rates 1.0–1.5–2.0–2.5C were chosen for cooling the batteries.

The best busbar material was identified as copper. Because these studies are long-term studies, it is also suggested to estimate the data obtained with ANN (Artificial Neural Networks). The purpose of ANN is to enable the solution of many different complex problems by creating systems that do not require human intelligence. Four different program (BR-LM-CGP-SCG) were used to estimate the data obtained with ANN. It was found that the most reliable algorithm was BR18. The R2 size of the BR18 algorithm in the test phase was 0.999552, the CoV size was 0.007697 and the RMSE size was 0.005076.

When the literature is considered, the cooling part of the battery modules has been taken into consideration during the temperature observation of the battery modules, but busbar materials connecting the batteries have always been ignored. In this study, various busbar materials were used and it was noticed how the temperature of the battery model changed under the same working conditions. These studies are very time-consuming and costly studies. Therefore, an estimation of the data obtained with artificial neural networks (ANN) was also evaluated.