Multi-objective Optimization of Plate Heat Exchanger for Commercial Electric Vehicle Based on Genetic Algorithm

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Plate heat exchanger (PHE) has been extensively utilized in cooling systems of commercial electric vehicles (CEVs). To enhance comprehensive performance, it is crucial to optimize the structural configuration of PHE. In this paper, a simplified model of the PHE from a CEV was numerically analyzed for thermal-hydraulic performance and verified by experimental data. After that, a multi-objective genetic algorithm optimization (MOGAO), where -j and f factors were set as objective functions, was implemented for its optimal structural configuration, and JF factor was proposed to assist the result selection. Finally, Optimization model 1 (OM1) was numerically compared with the original model under j, f, and JF factors to confirm enhancement. It is found that the simulation results could fit experimental data well within a reasonable error range. Among all the optimization results, OM1 owns the better JF factor. Compared with the original model, OM1 presents a higher average velocity and outlet temperature on the cold side. In detail, OM1 discovers the better comprehensive performance with a f decrement of 42.08%, j and JF increments of 5.02% and 21.53%, respectively.