Yazar "Morali, Ugur" seçeneğine göre listele

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    Nanofluid-based cooling of prismatic lithium-ion battery packs: an integrated numerical and statistical approach
    (Springer, 2023) Morali, Ugur; Yetik, Ozge; Karakoc, Tahir Hikmet
    Recently, the need for thermal management of lithium-ion batteries in electrical transportation engineering has received increased attention. To get maximum performance from lithium-ion batteries, battery thermal management systems are required. This paper quantitatively presents the efects of several factors on both maximum battery temperature and tem perature gradient. These factors include ambient temperature (288 K, 293 K, 298 K, 303 K, 308 K), C-rate (1C, 2C, 3C, 4C, 5C), mixing ratio (1%, 2%, 3%, 4%, 5%), and inlet velocity (0.01 m s ?1, 0.02 m s ?1, 0.03 m s ?1, 0.04 m s ?1, 0.05 m s ?1). Five levels for each parameter were considered to develop the orthogonal array. The signifcance of the variables was orderly shown through the L25 experiment. Results indicated that for maximal battery temperature, C-rate and ambient temperature are the most signifcant factors while for temperature gradient, C-rate and inlet velocity play an important role. For maxi mum battery temperature ambient temperature, C-rate, mixing ratio, and inlet velocity of 288 K, 1C, 4%, and 0.05 m s ?1, respectively, were obtained at the optimal setting. An ambient temperature of 308 K, a C-rate of 1, a mixing ratio of 5%, and an inlet velocity of 0.05 m s ?1 was the optimal setting for the temperature gradient. The results showed that the confrmatory test validates the optimization process for maximum battery temperature and temperature gradient. This study may provide a pathway for manufacturers and researchers interested in minimizing battery temperature and improving temperature gradient in electric vehicle applications.
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    A numerical study of thermal management of lithium-ion battery with nanofluid
    (Elsevier, 2023) Yetik, Ozge; Morali, Ugur; Karakoc, Tahir Hikmet
    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.