Numerical and artificial neural network inspired study on step-like-plenum battery thermal management system

Abstract

This study leverage numerical simulation (NS) and artificial neural network (ANN) capabilities to carry out additional investigations on step-like plenum battery thermal management system (BTMS). Different cooling strategies have been developed over the years in BTMSs’ design. Yet, air-cooling strategies still remains relevant, especially in battery-powered aircrafts, where light-weight is important and air is the preferred cooling fluid. Hence, additional study become necessary especially on the step-like plenum design to provide more insight on the performance of the design by considering several number of step, varied air inlet temperature and velocity. Computational fluid dynamics (CFD) approach was employed to obtained results for different number of step; N_s = 1,  3,  4,  7,  9,  15 and 19, varied air inlet temperature; T_i = 278,  298 and 318 K, and varied air inlet velocity; V_i = 3,  3.5,  4,  5 and 6 m/s. Artificial Neural Network (ANN) approach was then employed to predict the BTMSs’ performance for additional values of T_i and V_i. Minimum temperature (T_min), maximum temperature (T_max), maximum temperature difference (ΔT_max) and pressure drop (ΔP) were computed. By comparing the CFD results with the result predicted by the ANN, the percentage difference, for the entire dataset were 0.01 %, 0.005 %, 1 % and 0.14 % for T_max, T_min ΔT_max and ΔP, respectively. Based on the optimum design parameters predicted using ANN, for T_max = 299.24 comprises N_s = 4, V_i = 6 m/s and T_i = 278 K, while for ΔP, comprises N_s = 1, V_i = 3 m/s and T_i = 318 K.