1.1. Research Background
In the 20th century, fossil fuels were a key source of energy for industries globally. Consequently, the consumption of fossil fuels increased, leading to many problems, including the depletion of underground resources and environmental pollution due to exhaust emissions [
1,
2]. To address these issues, studies on new alternative energy sources are actively underway [
3,
4,
5,
6]. Among many alternative energy resources, fuel cells are eco-friendly and sustainable, with good energy efficiency. Based on these advantages, various global studies on fuel cells are ongoing [
7,
8,
9,
10]. Fuel cells using hydrogen as fuel are employed as diverse power sources, depending on capacity and characteristics. Polymer electrolyte membrane fuel cells (PEMFCs), offering advantages such as fast start-up characteristics, responsiveness, and high efficiency, are the most common type of fuel cells used in the mobility industry [
11,
12]. Substantial power output is required to drive commercial vehicles, given their significant load fluctuations and predominantly low-speed driving. Due to these characteristics and the mentioned advantages, commercial vehicles are equipped with PEMFC-type fuel cells as the power source [
13,
14]. Despite their advantages, PEMFC-type fuel cells face challenges in achieving optimal efficiency under high power output conditions, as their power output and efficiency exhibit inversely proportional characteristics with increasing current density. Choosing the appropriate trade-off between power output and efficiency is particularly challenging for large vehicles that require high power output in the low-current section compared to small vehicles [
15,
16]. Therefore, multi-stack systems are used in the field of heavy-duty mobility, which demands high power output [
17,
18,
19,
20,
21,
22,
23]. A multi-stack operation has these advantages. However, as stacks operate individually, cooling modules should be configured independently, or the cooling system actuators for heat dissipation need independent control. Furthermore, hydrogen-powered commercial vehicles operate as hybrids, with the fuel cell system and battery responsible for power output. These vehicles face the challenge of cooling the battery or power conversion system while driving. Consequently, thermal management systems and controllers that efficiently dissipate heat from hydrogen fuel cell multi-stacks, batteries, and power conversion systems significantly affect the efficiency of large vehicles. Therefore, thermal management systems and controllers are essential for improving efficiency.
1.2. Research Survey
To utilize fuel cell stacks in commercial vehicles, multi-stage stack technology, which ensures the high power required, should be applied. Therefore, many researchers are conducting case study research that considers various factors based on the supply of hydrogen and oxygen, thermal management, and electric structure for the efficient operation of multi-stage stacks.
Yahan et al. developed a hybrid power system model for a fuel cell and battery and proposed an on–off switch and power-following rule-based Energy Management System (EMS) to enhance energy efficiency in Fuel Cell Electric Vehicles (FCEVs) [
24]. Liang et al. proposed a self-learning energy management strategy for fuel cell hybrid vehicles to optimize hydrogen utilization and maintain battery operation. They applied the Fuzzy Reinforce algorithm to address EMS issues and validated the proposed method through Hardware-in-Loop experiments [
25]. Zixuan et al. provided a comprehensive overview of the advanced development of small-scale Proton Exchange Membrane Fuel Cells (PEMFCs) in the transportation, stationary, and portable power generator fields. They introduced various research cases in unmanned aerial vehicles, underwater vehicles, and light traction vehicles, as well as stationary and portable applications. The study highlighted current research trends and confirmed the practical application of these developments [
26].
Sondos et al. suggested that the method of constructing the electric structure of a multi-stage stack significantly affects the reliability, efficiency, and lifetime of the stack. The electric structure has serial, parallel, serial/parallel, and cascaded structures, and the results of their study showed that the serial/parallel structure produced the most rational results with the highest reliability and advantages of individual stack control [
27]. Marx et al. proposed the latest technology for multi-stage stacks and suggested various architectures for the electric structure and the supply of hydrogen and oxygen. They reported that the parallel structure produced the most efficient results for the supply of hydrogen and oxygen, whereas the serial/parallel structure produced the most efficient results for the electric structure [
28]. Neigel et al. conducted simulations using a power management system (PMS) that utilizes the fuel consumption of the multi-stage stack and battery systems and the battery charging status. In addition, they used the model they developed to verify the performance degradation of fuel consumption, fuel cells, and battery systems [
29]. Alexandre et al. developed a converter that considers the performance degradation and unbalanced operation of multi-stage fuel cells. Furthermore, they used two 500 W stacks to derive verification results for the converter they developed [
30]. Notably, the operating temperature of stacks is a strong dependent variable for performance, efficiency, and lifetime. Therefore, many researchers have conducted research on thermal management systems for managing the temperature of stacks over several years.
Ramezanizadeh et al. compared various cooling methods, including passive cooling, air cooling, water cooling, and phase change cooling, and analyzed the advantages and disadvantages of each cooling method in terms of cooling system efficiency [
31]. Choi et al. compared the two-phase HFE-7100 cooling method, which has high current density, with a single-phase water-cooling method and reported that the two-phase HFE-7100 cooling method produced more favorable results in maintaining the same temperature [
32]. Arash et al. created three types of metallic bipolar plate models and proposed varying cooling strategies depending on the shape [
33]. Chen et al. compared the cooling method using microencapsulated phase change suspension (MPCS) with the water-cooling method and analyzed the PEMFC thermal management system’s cooling performance based on the difference in cooling media [
34]. Ghasemi et al. built a model that simulates six cooling channels and conducted research on the local temperature distribution and temperature uniformity based on the model [
35]. Choi et al. utilized methods such as the performance curve method to experimentally investigate the effect of various operating conditions, which are important to the cooling performance of PEMFC systems, including the operating temperature, operating pressure, and relative humidity [
36]. Huang et al. developed a model that simulates a water-cooling system in a dynamic environment and proposed a control method that can optimize the temperature deviations that occur when the load changes [
37]. Alizadeh et al. designed a novel cooling strategy based on temperature uniformity, mass flow rate, pressure drop, and temperature differences between the inlet and the surface of the flow field. They used a numerical approach to conduct research on the thermal behaviors of different flow field models [
38]. Baek et al. designed six cooling flow fields and performed a numerical analysis of the temperature uniformity and pressure drop. According to the results, the serpentine flow field exhibited advantages in temperature uniformity compared to the parallel flow field [
39]. Cho et al. numerically investigated the flow of fluids and thermal behavior on cooling plates for the thermal management of stacks. They optimized the shape, which has been improved in terms of temperature distribution and flow uniformity, based on the numerical results [
40]. Afshari et al. conducted research on heat and temperature according to the flow channel method of PEM fuel cells. According to their research results, the zigzag-shaped flow decreased the maximum surface temperature, surface temperature differences, and temperature uniformity index by approximately 5%, 23%, and 8%, respectively, compared to the straight channel model. This result indicates that the zigzag-shaped flow operates effectively [
41]. Kandlikar et al. conducted a literature review on the thermal management of stacks and suggested that several components, such as catalyst particles and the microporous layer, are key design considerations for the thermal management of stacks [
42].
A thermal management system of stacks consists of a coolant pump, radiator, and cooling fan. However, the amount of heat dissipated varies according to the operation status of the pump and cooling fan, and a controller is essential to maintain the proper temperature of stacks.
Liso et al. developed a water-cooled PEMFC model to investigate temperature changes in response to rapid load variations in PEMFCs. Relatively slow temperature control affects the operational stability, such as degradation in the efficiency and performance of fuel cells and stack damage. They applied Feedforward control to control the flow rate of the coolant based on the current input [
43]. Hu et al. developed a model to control the temperature of a PEMFC cooling system and conducted research on tracking control from a temperature and energy perspective under different power levels. They compared the constant temperature control with the rule-based temperature control and verified that efficiency is improved through optimal temperature tracking control [
44]. Wang et al. utilized MATLAB/Simulink
® to develop thermal and electrochemical PEMFC models and used fuzzy control rules to regulate the stack’s temperature [
45]. O’Keefe et al. constructed a cooling system model for a 5 kW fuel cell system. They applied a proportional–integral (PI) controller with the coolant flow rate as the target to control the operating temperature of the fuel cells [
46]. Cheng et al. conducted research on model-based temperature regulation of a PEMFC system for a city bus. They configured the pump to operate at a constant flow rate to reduce variables in temperature regulation. In addition, they applied the Feedforward and Feedback controllers to control the temperature of the coolant passing through the radiator fan [
47]. Saygiliet et al. developed a model based on reference papers to cool a 3 kW PEMFC. They proposed three strategies for controlling the operating temperature of fuel cells through combining the on/off model and the PI controller [
48].
However, the previous studies mostly compared the performance of multi-stage stacks and focused only on the cooling efficiency based on factors such as the material of the stack cooling system, shape of the cooling flow channel, and operating conditions. Furthermore, there has been inadequate research on power distribution strategies for efficiently operating the stack and battery of the overall hybrid system that includes a battery.
Moreover, the performance of PEMFCs heavily depends on the electrochemical exothermic reaction [
49,
50] and parameters such as the operating temperature, relative humidity, and stoichiometric ratio [
51,
52,
53]. These parameters become unstable owing to the heat generated from the electrochemical reaction. Consequently, it is difficult to maintain consistent performance. In particular, the amount of heat generated varies in real time; thus, controlling the cooling system, which maintains consistent operating conditions, is important. Large vehicles, such as hydrogen electric trucks and hydrogen-powered buses, require power more than twice that of passenger cars, which is over 100 kW. Although large hydrogen electric trucks are heavy, they are advantageous in terms of ram air because they often travel at high speeds. However, when such a truck is traveling slowly and requires more power, as in uphill driving, the vehicle does not benefit from ram air. Therefore, the power consumption of the cooling system increases, leading to degradation of the vehicle’s performance.
Therefore, in this study, a hydrogen electric truck system model was developed based on a Hyundai Xcient hydrogen electric truck using MATLAB/SimscapeTM 2022b to analyze the performance of hydrogen electric trucks. MATLAB/SimscapeTM 2022b implements physical simulations for the development of the system model. It also facilitates the connection of various physical signals, such as electricity, heat, fluid, and vehicle dynamics, to domains.doc-int.