Multi-objective optimization of multi-channel cold plate under intermittent pulsating flow by RSM and NSGA-Ⅱ for thermal management of electric vehicle lithium-ion battery pack

Abstract

The heat transfer and energy consumption characteristics are the most important performance parameters of cold plate for thermal management of electric vehicle lithium-ion battery pack. In this work, in order to address the issue about multi-objective optimization of multi-channel cold plate under intermittent pulsating flow, RSM (Response Surface Methodology) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) is combined to make a trade-off between the average heat transfer coefficient and energy consumption of multi-channel cold plate under intermittent pulsating flow. Box-Behnken design is used to arrange a series of numerical investigations using the steady flow velocity vin, pulsation amplitude A, and pulsation frequency e as design variables and the average heat transfer coefficient have and energy consumption W as objective functions. Regression models are created in the form of quadratic polynomials, and the significance of each term in the model is determined by analysis of variance (ANOVA). Results show that the linear term of vin has the greatest effect on have and W. According to the Pareto optimal solution obtained from NSGA-II, the optimal objective functions are have = 394.7012 W m−2 °C−1, W = 0.1086 J, and the corresponding design variables are vin = 0.02392 m/s, A = 0.1778 and e = 3.1846 Hz.