## 1 Introduction  
Fuel cell electric vehicles (FCEVs) are expected to provide short refueling times and a long range to modern drivers [1]. Manufactures and drivers, on the other hand, have placed significant demands on technical issues including economy, stack durability, vehicle efficiency, and thermal management [2]. The quantity of heat generated by the stack increases as the vehicle's weight increases due to its high-power density. Due to the heat generated by the electrochemical reaction, maintaining high performance is exceedingly difficult and is a critical factor defining system efficiency, availability, and reliability. To solve these technical challenges, a high-performance thermal management system based on a louver fin type of compact heat exchanger (LFHX) is required [3, 4]. The size and weight of the heat exchanger are particularly critical in large fuel cell vehicles. This is because the greater the drag force on the vehicle, the poorer the fuel economy and, as a result, the performance. Many researchers have conducted various optimization studies to achieve miniaturization, high heat dissipation, and the low annual cost of the heat exchanger. To derive design parameters that satisfy single or multiple goals, optimization design techniques such as particle swarm optimization (PSO), genetic algorithm (GA), artificial bee colony algorithm (ABCA), and imperialist competitive algorithm (ICA) were typically used [5, 6]. [PERSON] et al. [7] extended the PSO method applied to the shell and tube type heat exchanger to the plate fin heat exchanger and achieved better results than the GA method by considering the total weight and annual cost of the heat exchanger as the objective function. Moreover, [PERSON] et al. [8] applied a hybrid type of optimization algorithm that applied a bees algorithm (BA) to PSO to a plate fin heat exchanger to improve the disadvantages of simply falling into rocker optimization or low optimization precision when the dimension of the objective function is high or local optimization exists. [PERSON] et al. [9] applied ICA, which is based on human socio-political evolution, to shell and tube heat exchangers and presented its excellence through a case study. [PERSON] et al. [10] achieved excellence in terms of cost optimization through a case study for shell and tube heat exchangers using ABCA. GA, which is the most comparable in optimization studies, is an algorithm that mimics the natural selection process of biological evolution and is used in various types of heat exchangers. [PERSON] et al. [11] optimally designed an LFHX to be used in a small truck using GA and analyzed the parameters that affect heat dissipation through sensitivity analysis. [PERSON] et al. [12] used the \(<\)second generation of nondominated sorting genetic algorithm (NSGA-II) \(>\) to derive an optimal solution through multipurpose functions of heat release and annual cost. NSGA-II is known as an algorithm that strengthens elitism and effectively maintains solution diversity through nondominated sorting and crowding distance assignment methods. Previous studies have suggested the feasibility of heat exchanger optimization design by analyzing the effect of each design parameter, such as sensitivity analysis; however, various questions remain, and detailed formulas to confirm this and the omission of optimized key design parameter values are needed. Therefore, this paper presented geometric shape information obtained by optimizing the design information of the heat exchanger presented in Ref. [11]. In addition, the dynamic characteristics of the heat exchanger were analyzed by applying the driving cycle of the vehicle. The heat exchanger of eco-friendly hydrogen electric vehicles is still being studied because it is very important, and the optimal design study should evaluate and analyze the results within the heat management system for parts.