Innovative-serpentine cooling method of batteries: Both thermal and statistical method approach


Yetik Ö.

INTERNATIONAL JOURNAL OF THERMAL SCIENCES, cilt.208, 2025 (SCI-Expanded) identifier identifier

Özet

Renewable energy, in particular, is critical for a sustainable world. Effective storage of energy is at the heart of all systems. While energy storage allows us to preserve the beauties offered by nature, it also requires innovative solutions that push the limits of technology. The most important factor affecting the performance of batteries is their temperature. For this reason, the serpentine cooling model, which is an innovative battery cooling method, was evaluated in this study. Generally, in the literature, batteries are considered as heat masses and given a certain heat flux and their temperature distributions are examined. In this study, batteries were connected to each other with busbars as in reality and thermal analyses were performed. In addition, a serpentine cooling method, which has never been used before, was tried as a cooling method in batteries. While all these evaluations were made, statistical analyses were performed for the priority order of the parameters used. All of these situations show how innovative the study is. The NTGK model was used in CFD analyses. 5 different parameters were considered. These are the discharge rate (0.5C, 1C, 1.5C, 2C and 2.5C), the type of refrigerant (air and water), the speed at which the refrigerant enters the model (0.01 m/s, 0.03 m/s and 0.05 m/s), the ambient temperature (293K, 298K and 300K), and the SOH value (50 %, 65 %, 75 %).Water has been shown to be a better refrigerant than air. As the inlet speed of the refrigerant was increased, the discharge rate was reduced, and the SOH value decreased, the temperature values obtained by the model were lower. The temperature values of the batteries according to their location in the model were also examined. 5-factor, 2-level experiments were conducted to examine statistically. It was checked whether the created values fit the distributions and it was seen that the most effective parameter used in the model was the type of refrigerant. In addition, the most statistically effective working conditions were also determined.