A Study on the Optimization of the Louver Fin Heat Exchanger for Fuel Cell Electric Vehicle Using Genetic Algorithm

Abstract

Fuel cell electric vehicles offer a short fuel charging time and high mileage, but require precise thermal management technology to ensure the durability and efficiency of the stack. Accordingly, the size and weight of the heat exchanger increase to ensure the performance of the heat exchanger. For this reason, a louver fin type heat exchanger requires optimal size and weight, as well as high performance. This paper optimally designs high-performance heat exchangers with reduced size and weight by applying genetic algorithms to solve this problem. The optimal result value was achieved by optimizing the design variables using concentrated variable modeling and a genetic algorithm, and the dynamic characteristics of the heat exchanger were analyzed by applying the driving cycle of the vehicle. In addition, 3D modeling was conducted to present the weight and practically applicable form. As a result, compared to the existing model, the heat transfer rate and effectiveness were improved by 5% and 1.6%, respectively, and the weight was also reduced by 5.8%. These results exceed the expected performance improvements of low size and weight. Moreover, it is expected that an improved heat exchanger optimization, as well as a design reflecting a drive cycle, could be conducted using the proposed genetic algorithm and be applied not only to a heat exchanger, but also to various components.

Keywords

• genetic algorithm
• heat exchanger
• radiator
• optimization
• lumped parameter modeling
• drive cycle