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oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/127312026-10-18T20:36:36Zopenairedrivercom_ebyu_10000col_ebyu_10001

Multi-objective optimization of a mini channeled cold plate for using thermal management of a Li-Ion batteryKalkan, OrhanCelen, AliBakirci, KadirBattery thermal managementCold plateLi-ion batteryMini channelOptimizationBattery management systemsCoolantsDesign of experimentsMultiobjective optimizationTemperature controlThermal management (electronics)Battery temperatureBattery thermal managementsCold platesCoolant flowrateMini-channelsMulti-objectives optimizationOptimisationsOptimum solutionThermalThermal management systemsenergy managementfuel cellregression analysisLithium-ion batteriesMini channeled cold plates (MCCP) are an impressive choice for electric vehicles with a liquid-based thermal management system to keep the battery temperature within the desired range and to achieve a homogeneous temperature distribution. This paper came up with an optimum solution for maximum battery temperature (MBT), maximum temperature difference on the battery surface (MTD) and pressure drops in the channels (ΔP), which are vital parameters. Multi-objective optimization of geometric parameters and coolant flow rate of a designed MCCP which is experimentally investigated is carried out using the Desirability Function Approach. Firstly, the design of experiments (DoE) is generated using response surface method after determining the input variables and their levels. Then, each sample of DoE are modeled and analyzed numerically. Furthermore, the regression equations of the objective functions (MBT, MTD and ΔP) depending on the design parameters are developed and their variance analyzes are performed. Prediction accuracies of model equations developed for MBT, MTD and ΔP are 97.75%, 95.49% and 97.15%, respectively. As a result, optimum values of design variables, namely channel width (mm), distance between branches (mm), channel depth (mm), number of crossovers in branches and coolant flow rate (l/min) are 5, 5, 10, 13 and 0.7061, respectively. © 2022Elsevier Ltd2022Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/12731https://doi.org/10.1016/j.energy.2022.1239490360-5442enEnergyinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/12731