Development of a 470-Horsepower Fuel Cell–Battery Hybrid Xcient Dynamic Model Using Simscape^TM

Abstract

Polymer electrolyte membrane fuel cells (PEMFCs) are employed in trucks and large commercial vehicles utilizing hydrogen as fuel due to their rapid start-up characteristics and responsiveness. However, addressing the requirement for high power output in the low-current section presents a challenge. To solve this issue, a multi-stack can be applied using two stacks. Furthermore, thermal management, which significantly affects the performance of the stacks, is essential. Therefore, in this study, a hydrogen electric truck system model was developed based on a Hyundai Xcient hydrogen electric truck model using MATLAB/Simscape^TM 2022b. In addition, the system’s performance and thermal characteristics were evaluated and analyzed under different road environments and wind conditions while driving in Korea.

1. Introduction

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/Simscape^TM 2022b to analyze the performance of hydrogen electric trucks. MATLAB/Simscape^TM 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.

2. System Configuration

The hydrogen electric truck system model, depicted in Figure 1, is based on a Hyundai Xcient truck and comprises two 90 kW fuel cell stacks, three 24 kWh batteries, a 350 kW/2237 Nm motor, hydrogen fuel tanks, compressor, water pump, large-capacity radiator, cooling fan, three-way valves, PMS, and respective controllers [54,55].

Table 1. Fuel cell system specifications.

System	Components	Parameters	Unit
Fuel processing system	Number of tanks	7	ea
	Hydrogen tank pressure	70	MPa
	Hydrogen tank temperature	293.15	K
	Hydrogen volume in single tank	80	L
	Hydrogen mass in single tank	4.5	kg
	Mole fraction of hydrogen in tank	0.9997	-
Fuel cell stack	Number of cells	545	ea
	Active area	280	cm2
	Membrane thickness	125	μm
	Anode gas diffusion layer	250	μm
	Cathode gas diffusion layer	250	μm
	Exchange current density	0.00008	A/cm2
	Limiting current density	1.4	A/cm2
	Charge transfer coefficient	0.5	-

lists the specifications for the fuel cells and hydrogen supply system. Additionally, the air supply system was developed based on compressor data obtained from a reference paper on compressors used in hydrogen vehicles.

2.1. Hydrogen Supply System

In most studies, the parallel method is selected for the hydrogen supply system of multi-stage stacks to enhance safety and durability [56,57,58,59,60]. Furthermore, direct hydrogen supply from a 700-bar tank can lead to stack damage [61]. Therefore, the hydrogen supply system was designed to deliver hydrogen through two decompression processes, as illustrated in Figure 2 [62]. Additionally, hydrogen tanks were developed based on data from a Hyundai Xcient hydrogen truck [63].

2.2. Air Supply System

Air supply systems used in commercial vehicles, such as trucks, remain unaffected by the external environment. To achieve high power output, pressurized air is supplied through a high-pressure compressor [64,65,66]. Centrifugal compressors, preferred for their advantages in efficiency, lifetime, and response speed, are commonly utilized [67,68,69,70,71]. Consequently, this study applied experimental data to the compressor model used in hydrogen electric trucks, as depicted in Figure 3 [67].

2.3. Fuel Cell Stack

Fuel cell stacks generate power and heat through the electrochemical reaction of hydrogen and oxygen. However, loss of voltage occurs owing to various reactions. The open circuit voltage (OCV), the theoretical reversible voltage of a fuel cell stack, is expressed below. The following equation is called the Nernst equation:

$$\mathrm{E}=-\frac{\Delta g_f}{nF}=-\frac{\Delta g_f^0}{nF}+\frac{RT}{nF}ln\left(\frac{p_{H_2}{p}_{O_2}^{0.5}}{p_{H_{2}O}}\right)$$

(1)

The voltage loss in actual fuel cell stacks can be classified into three types: activation overpotential generated through the charge transfer process, concentration overpotential generated owing to changes in the concentration resulting from the movement of reactants in the stack, and ohmic overpotential generated as ions or electrons move. These can be expressed as follows:

$$V_{act}=\frac{RT}{n\alpha F}ln\left(\frac{i}{i_0}\right)$$

(2)

$$V_{conc}=\frac{RT}{nF}ln\left(1-\frac{i}{i_L}\right)$$

(3)

$$V_{ohm}=i\times R_{ohm}$$

(4)

Consequently, the actual voltage generated in the fuel cell stack can be summarized as shown below. Additionally, the power generated in the fuel cell stack can be calculated using the derived voltage.

$$V_{cell}=E-V_{act}-V_{con}-V_{ohm}$$

(5)

$$P_{elec}=V_{cell}\times i\times \mathrm{n}$$

(6)

The amount of heat generated by the fuel cell stack can be calculated using the difference between the net power generated in the stack and the electrically generated power. Figure 4 shows the voltage and power curves according to the current density for the fuel cell stack developed in this study.

$$\dot{Q}=P_{net}-P_{elec}$$

(7)

2.4. Battery

Batteries and fuel cell stacks are utilized as a hybrid system in the power supply system of hydrogen electric vehicles, owing to the slow dynamic characteristics of the fuel cells [72,73,74]. The batteries used in the hydrogen electric truck consist of three 24 kWh capacity lithium-ion high-voltage batteries connected in series.

Table 2. Battery System Specification [63].

System	Components	Parameters	Unit
Lithium-ion battery	Nominal voltage	630	V
	Energy capacity	24	kWh
	Number of batteries	3	ea

presents the detailed performance specifications.

Figure 5 illustrates the results of the battery model. Increasing current was applied to the battery for 3600 s, resulting in a power output of 72 kW. Additionally, it can be observed that the margin of decrease in the state of charge (SOC), indicative of the charging status of the battery, gradually increases as the applied current increases.

2.5. DC–DC Converter

The DC–DC converter is a device that converts direct current (DC) power to a constant required voltage, preventing vehicle performance degradation owing to changes in the voltage [75,76]. Furthermore, because the direction of power varies according to the SOC, a bidirectional converter was used for the battery, while a unidirectional converter was used for the fuel cell stack.

In this study, the target voltage was set to 1000 V considering the system current and voltage specifications, and the results can be found in Figure 6. It can be observed that the target voltage is maintained even when the input voltage varies, and the voltage converges within 0.02 s.

2.6. Powertrain System

The powertrain system generates power for driving through the power supply system within the vehicle, and consists of a motor and a reducer, which propel the wheels to drive the vehicle.

2.6.1. Motor

A permanent magnet synchronous motor (PMSM) is employed as the motor in most electric vehicles due to its efficiency, high power output, and acceleration performance. Hence, a maximum torque of 2237 Nm and a maximum power output of 350 kW were applied based on the data of the motor equipped in a Hyundai Xcient truck, as shown in

Table 3. PMSM Specification [63].

System	Components	Parameters	Unit
PMSM	Maximum Torque	2237	Nm
	Maximum Power	350	kW

. The performance curve can be found in Figure 7.

2.6.2. Reducer

The motor operates at a high rotational speed and low torque. Therefore, a reducer is used to convert them into the rotational speed and torque required to drive the vehicle. The vehicle’s torque and rotational speed are converted according to the gear ratio of the PMSM’s base gear and the reducer’s follow gear, and the gear ratio is calculated using the following equation:

$$g_{ratio}=r_f{\omega }_f=r_b{\omega }_b$$

(8)

here, $g_{ratio}$ denotes the gear ratio of the reducer, $r$ denotes the radius of the gear, and $\omega$ denotes the angular velocity of the gear. Currently, the Xcient vehicle can be driven with a gear ratio ranging from 1 to 15.78 using a 12-speed manual transmission [77]. However, the gear ratio was set to eight for driving in this study [78].

Figure 8 illustrates the output torque and rotational speed for the motor and reducer according to the vehicle’s speed. It can be observed that the torque value of the reducer, in comparison to the motor, increases while the rotational speed decreases according to the gear ratio of the reducer.

2.6.3. Powertrain

To develop a powertrain system model that considers driving resistances (including the weight of the vehicle, the slope of the road, and wind), traction force, brake, and driving resistances were calculated based on the motor and brake speed controller, PMSM, the reducer model, and the vehicle specifications in

Table 4. Xcient Vehicle Specification.

System	Components	Parameters	Unit
Vehicle specification	Vehicle mass	28,000	kg
	Tire rolling radius	11.25	In
	Tire rolling coefficient	0.008	-
	Air drag coefficient	1.15	-
	Vehicle front area	2.54 × 3.73	m2
	Gravitational acceleration	9.81	m/s2

[63] using the force balance equation.

$$\sum F_{vehicle}=F_{drive}-F_{brake}-F_{resist}$$

(9)

$$F_{drive}=\frac{{\tau }_{axle}}{r_{tire}}$$

(10)

$$F_{brake}=F_B\tanh \left(\frac{{\omega }_{axle}}{{\omega }_1}\right)$$

(11)

$$F_{resist}=\left(F_{tire}\cos \theta +F_{air}\right)\tanh \left(\frac{v_x}{v_1}\right)+mg\sin \theta$$

(12)

here, $F_{drive}$ denotes the traction force based on the torque and the radius, $F_{brake}$ denotes the force applied to the brake, and $F_{resist}$ denotes the resistance force against a moving vehicle owing to the rolling resistance of the tires, air resistance, and gradient resistance.

The velocity, slope of the road, and wind were applied, as shown in Figure 9a, to evaluate the developed powertrain model. After 5000 s, it can be observed that for the same speed, it appropriately followed the target velocity according to the changes in the slope of the road. Figure 9b shows the input information for the accelerator and brake pedals to follow the target velocity. Figure 9c shows the rotational speed and torque of the motor and reducer for driving the vehicle. Furthermore, the required torque can be observed to vary based on the slope of the road, and whether the vehicle is being driven on flat land, a downhill road, or an uphill road.

2.7. Thermal Management System

There are limitations in using the components of an actual Xcient vehicle to conduct an experiment with the thermal management system.

Therefore, the thermal management system was developed based on previous studies that can verify the performance of the cooling components of a hydrogen electric vehicle [79].

The radiator was configured using a louver-fin heat exchanger model, developed to cool the high-temperature coolant used for cooling the stack, with air passing through the vehicle’s ram air and cooling fan.

$$\mathsf{\epsilon }=\frac{1-\exp \left[-NTU\left(1-c\right)\right]}{1-C \exp \left[-NTU\left(1-c\right)\right]}$$

(13)

$$\mathrm{NTU}=\frac{1}{c_{min}\times R}$$

(14)

The amount of heat transfer was calculated using the determined heat transfer effectiveness, as follows:

$$\dot{Q}=\epsilon \times {\dot{Q}}_{max}$$

(15)

$${\dot{Q}}_{max}=\frac{c_{min}}{\left(T_{coolant,in}-T_{air,in}\right)}$$

(16)

Consequently, the outlet temperature of the fluid was calculated as follows:

$$T_{coolant,out}=T_{coolant,in}-\frac{\dot{Q}}{c_{coolant}}$$

(17)

$$T_{air,out}=\frac{\dot{Q}}{c_{air}}+T_{air,in}$$

(18)

As illustrated in Figure 10, the water pump and the cooling fan were developed based on the performance map data obtained from a previous study [79]. However, in the case of the cooling fan, the pressure drop through the ram air changes continuously owing to the varying velocity, which changes as the vehicle drives. The airflow rate is determined by the pressure drop conditions and the speed of the motor, as depicted in Figure 10b. Hence, the calculation of the pressure drop through the ram air is a crucial factor. The pressure drop encompasses both external and internal factors. The ram air constitutes an external factor, while the front grille of a vehicle, radiator, and condenser are internal factors. Due to the pressure drop occurring owing to these factors, they should be considered in thermal management. Therefore, the pressure drop due to each factor and pressure increase through the ram air are calculated using the following equations [80]:

$$\Delta P_{rise}=\Delta P_{grill}+\Delta P_{cond}+\Delta P_{rad,air}-\Delta P_{ram}$$

(19)

$$\Delta P_{grill}=0.5C_{p,grill}{\rho }_{air}{V}_{grill}^2$$

(20)

$$\Delta P_{ram}=0.5C_{p,ram}{\rho }_{air}{V}_{veh}^2$$

(21)

$$\Delta P_{cond}=0.5\Delta P_{rad}$$

(22)

The pressure drop of the air through the radiator’s louver fins is provided by Kays and London [81,82].

$$\Delta P_{rad,air}=\frac{G^2}{2{\rho }_i}\left[\left({\kappa }_c+1-{\sigma }^2\right)+2\left(\frac{{\rho }_i}{{\rho }_o}-1\right)+f\frac{A}{A_{min}}\frac{{\rho }_i}{\rho }-\left(1-{\kappa }_e-{\sigma }^2\right)\frac{{\rho }_i}{{\rho }_o}\right]$$

(23)

$$A_t/A_{min}=\frac{4L}{D_h}\left(Total heat transfer area/minimum flow rate\right)$$

(24)

$$G=\frac{\rho V_{veh}{A}_{fr}}{A_{min}}$$

(25)

The current density of the stack was set to 0.7 A/cm² to assess the effect of the cooling fan differential pressure. Figure 11 illustrates the changes in the cooling fan’s performance. In Figure 11a, the stack temperature rises due to the increased current density. However, the cooling system effectively regulates the temperature. In Figure 11b, at 0 km/h vehicle velocity, the inlet pressure of the cooling fan is low, resulting in a significant difference between the inlet and outlet pressures. Additionally, the rotational speed varies for the same flow rate between 500 and 1500 s due to the pressure difference in that segment.

3. Results and Discussion

The performance characteristics and thermal behavior of the hydrogen electric truck system developed in this study were evaluated through applying driving cycles based on the road slope and wind conditions in Korea.

3.1. Xcient Dynamic Simulation Model

Figure 12 depicts the Xcient-based dynamic simulation model. The power required by the motor is generated based on the required speed, and this required power is distributed to both the battery and the fuel cell stack through the PMS, thereby generating a load. The distributed power is then converted to the same voltage level through a converter and input to the motor. Furthermore, the Xcient-based dynamic simulation model includes a thermal management system responsible for regulating the temperature of the stack.

3.2. Simulation Scenario

To validate the developed hydrogen truck model, a driving cycle was created, as depicted in Figure 13, based on a study evaluating the slopes of domestic roads [82,83]. The entire driving cycle spans 9000 s, during which the vehicle is in a driving or moving state for 7200 s and in a stationary state for 1800 s. In urban areas, the vehicle travels at a speed of 90 km/h on roads with a slope of 0°. In mountainous regions, the vehicle travels at speeds of 60 km/h and 30 km/h on roads with slopes of 3° and 5°, respectively. Additionally, the system’s performance was analyzed using wind speed data sourced from the Korea Meteorological Administration, categorized by region and season, as presented in

Table 5. Wind Speed with Korean Season and Region [84].

Season	Region	Parameters	Unit
Summer	Mountain	1.9	m/s
	Urban	2.2	m/s
Winter	Mountain	3.3	m/s
	Urban	2.4	m/s

[84].

3.3. PMS

The power supply system of the hydrogen electric truck consists of fuel stacks and a battery, and an efficient power distribution system is essential to prevent overall system performance degradation and extend its lifetime. In this study, the system was configured to distribute power based on the battery SOC and load power, as shown in Figure 14. The load power of the battery and fuel cells changes based on the SOC’s upper limit and lower limit states. Thereafter, power is distributed to each power supply device based on the load power. Figure 15 illustrates the results of the PMS, demonstrating power generation by the battery and fuel cells in a distributed manner owing to the power distribution system. It can be observed that the battery is charging at the 7000 s mark when the battery’s SOC is below the lower limit state, and the required power has decreased.

3.4. Response Characteristics of the Cooling System Considering Ram Air

Figure 16 and Figure 17 depict the response characteristics of the fuel cell stack’s cooling system, considering ram air during domestic driving in the summer and winter seasons, respectively. The inlet and outlet temperatures of the multi-stage stack were set as the control targets to manage the temperature of the stack. As illustrated in Figure 18, it was assumed that Stack 1’s outlet and Stack 2’s inlet were adiabatically treated. Additionally, the cooling fan and water pump were controlled to regulate the inlet of Stack 1 and the outlet of Stack 2 at 333.15 K and 343.15 K, respectively. It can be verified that the cooling fan operates in all driving segments to regulate the stacks’ temperature, and the cooling fan operates at a minimal rotational speed in segments where the temperature of the stacks is less than or equal to the target temperature. Moreover, Figure 19 illustrates changes in the vehicle’s demand power due to wind speed in the summer and winter seasons. The wind speed is 1.4 m/s higher in the winter season than in the summer season. Therefore, compared to summer driving, winter driving requires a power output that is 9.5% higher on average in all segments.

4. Conclusions

In this study, a model was developed to evaluate the cooling system of the fuel cell system in a 470-horsepower fuel cell–battery hybrid truck. The model was assessed based on the slopes of domestic roads and wind speeds by season. It was verified that the load power for the required speed is distributed through the developed PMS and through the fuel cells and battery, respectively. The main results of this study are summarized as follows:

• To develop the fuel cell–battery hybrid truck model, two 90 kW fuel cell stack modules, a hydrogen supply system, and an air supply system were created based on the Hyundai Xcient hydrogen truck data. Furthermore, a 72 kWh battery pack model was developed using three 24 kWh batteries.
• The model was designed to calculate the motor’s RPM and torque for the target speed through considering the rolling resistance, air resistance, and gradient resistance that occur during the operation of an Xcient hydrogen truck.
• The driving results based on the slopes of domestic roads and seasons verified the power distribution of the stack and battery based on the load power and the battery’s SOC through the developed PMS. Furthermore, it was confirmed that the fuel cell stack operates to charge the battery when the load power is low and the SOC is below the lower limit.
• The power outputs required to drive the vehicle were compared based on the summer and winter wind data for the same speed and slope. It was confirmed that the demand power to drive the vehicle in winter increases by 9.5% on average owing to strong winds compared to summer.