Multi-objective optimization for an active air-cooling thermal management system

A multi-objective optimization using genetic algorithm (GA) was applied to optimize the parameters of an active air-cooling thermal management system (TMS) for battery packs to achieve high efficiency of battery pack cooling. The objectives of the optimization were to maximize the heat transfer rate (represented by the convection heat transfer coefficient - h) and minimize the required power to drive the air-cooling system (represented by the pressure drop - ∆p). The control parameters in the optimization were the velocity of air flow (V_air), the transverse and longitudinal pitch between battery centers (S_T, S_L), and the battery pack arrangement (aligned/staggered). Žukauskas model of flow across a bank of tubes was used to determine the values of h and ∆p. The combined algorithms were written in MATLAB code and the optimization was performed using MATLAB GA multi-objective optimization. The results of optimization (values of control parameters that achieve the best compromised solution) were determined for both the aligned and staggered arrangements.