A numerical study of thermal management of lithium-ion battery with nanofluid.

In this study, the NTGK model was used to evaluate the thermal and electrical analyzes of the battery model and Taguchi design was implemented to investigate the main effects of four control factors in the battery thermal management process, those are inlet velocity, mixing ratio, ambient temperature, and C-rate. The Taguchi's L16 array was fabricated using varying control factors to obtain detailed battery temperature behaviors. As the discharge rate increased, the temperature value of the model increased, while the temperature value of the model decreased as the mixing ratio of the nanoparticle increased. As the inlet velocity of the refrigerant increases, the temperature value taken by the model decreases, while the higher the ambient temperature, the less the increase in the maximum temperature reached by the model. Also results showed that the most influential factor on both maximum battery temperature and temperature uniformity responses was the C-rate, while the least effective factor was the mixing ratio. It was found that an inlet velocity of 0.04 m/s, a mixing ratio of 5, a C-rate of 2, and an ambient temperature of 283 K will yield the lowest maximum battery temperature. The maximum battery temperature was 294 K under these conditions. On the other hand, to maximize the temperature uniformity, 0.04 m/s inlet velocity, 3 mixing ratio, 2 C-rate, and 313 K ambient temperature need to be set as processing parameters. The results showed that the C-rate has to be closely controlled during the discharge process and the influence of the mixing ratio is negligible. This study can be used as a robust guideline in the design of battery thermal management systems using nanofluids. • Thermal behavior of lithium-ion battery pack has been studied statistically by the Taguchi method. • Impacts of ambient temperature, C-rate, mixing ratio, and inlet velocity were studied for battery temperature. • C-rate was the main influential factor among the four assessed factors. • The confirmatory test validates the optimization process. [ABSTRACT FROM AUTHOR]