Comparative Evaluation of Power Management Strategies in Multi-Stack Fuel Cell-Battery Hybrid Truck via TOPSIS

Abstract

Multi-stack Polymer electrolyte Membrane Fuel Cell (PEMFC) systems are increasingly adopted in heavy-duty mobility to overcome the power limitations and thermal instability of single-stack configurations. However, the overall energy efficiency, hydrogen utilization, and thermal behavior of multi-stack fuel cell trucks are highly dependent on the applied Power Management System (PMS). In this study, high-fidelity, system-level dynamic model of multi-stack fuel cell truck was developed using Matlab/Simscape^TM, and three PMS approaches (rule-based control, state-machine control, and fuzzy logic control) were comparatively evaluated. The analysis includes coolant temperature regulation, hydrogen consumption, battery State of Charge (SoC) dynamics, and the parasitic power demand of Balance of Plant (BoP) components. Results show that the fuzzy logic PMS provides the most balanced operating profile by smoothing transient fuel cell loading and actively leveraging the battery during high-demand periods. In the thermal domain, the fuzzy logic PMS reduced temperature overshoot by up to 61.20%, demonstrating the most stable thermal control among the three strategies. Hydrogen consumption decreased by 3.08% and 0.89% compared with the rule-based and state-machine PMS, respectively, while parasitic power consumption decreased by 7.12% and 3.32%, confirming improvements in overall energy efficiency. TOPSIS-based multi-criteria decision analysis further showed that the fuzzy logic PMS achieved the highest closeness coefficient (0.9112), indicating superior system-level performance. These findings highlight the importance of PMS design for achieving energy-optimal and thermally stable operation of multi-stack PEMFC trucks and provide practical guidance for future control strategies, heavy-duty mobility applications, and next-generation hydrogen powertrain optimization.

1. Introduction

1.1. Research Background

Internal combustion engine (ICE) vehicles still dominate the global automotive market, accounting for nearly 75% of total sales, while battery electric vehicles (BEVs) represent around 16.2%, plug-in hybrid electric vehicles (PHEVs) about 8.7%, and hydrogen-powered vehicles less than 0.1% [1,2,3]. However, the continued reliance on fossil-fuel-based transportation has intensified environmental challenges such as greenhouse gas emissions and urban air pollution, leading governments worldwide to impose increasingly stringent emission regulations [4]. This shift has accelerated the transition toward eco-friendly mobility technologies, including BEVs and hydrogen electric vehicles (HEVs).

Despite the rapid progress of battery technology, heavy-duty applications, such as long-haul trucks, pose unique challenges. They require extended driving ranges and high payload capacities, which in turn demand substantial onboard energy storage. This often results in a significant increase in battery weight, thereby compromising payload capacity and vehicle efficiency [5,6]. In such cases, hydrogen-powered fuel cell vehicles present a more practical solution due to their higher energy density and shorter refueling time.

PEMFC is particularly attractive for heavy-duty transport, offering advantages such as high efficiency, rapid start-up, low acoustic noise, and zero tailpipe emissions [7,8]. Additionally, they are well suited to applications with high power demand and frequent load fluctuations, conditions commonly encountered in commercial freight operations [9,10]. However, despite these advantages, fuel cell technologies still face several challenges in commercial applications. In particular, during long-term operation, fuel cells experience gradual performance degradation caused by a combination of electrode material degradation, catalyst loss, mechanical damage to the membrane electrode assembly (MEA), and variations in operating conditions. Such degradation not only reduces efficiency and power output but also shortens system lifetime and increases maintenance and replacement costs [11]. These issues become more pronounced in heavy-duty freight applications, where high loads and frequent load transients are unavoidable. To mitigate these limitations, multi-stack (multi-reactor) fuel cell architectures have recently attracted growing attention. By distributing power demand across multiple stacks, multi-stack configurations can alleviate localized thermal and electrical stresses, thereby improving system reliability and extending operational lifetime. This scalability makes PEMFC systems suitable for high-power applications ranging from compact single-stack systems to large-scale multi-stack configurations [12,13,14]. Nevertheless, single-stack systems often fall short of meeting the stringent power requirements of heavy-duty vehicles [15,16].

To overcome these limitations, multi-stack fuel cell systems have been introduced to enhance power output, durability, efficiency, and overall system reliability [17,18,19]. In particular, they provide significant thermal management advantages over single-stack systems, which typically suffer from limited natural convection cooling capacity and require additional energy for heat removal. Effective thermal control strategies are therefore crucial to maintaining optimal operating conditions and minimizing auxiliary power consumption. Furthermore, to address the inherently slow dynamic response of fuel cells, hybrid architectures combining PEMFC with auxiliary battery system is widely adopted. Such configurations improve system responsiveness, support rapid load transitions, and extend fuel cell lifetime. Consequently, an advanced Power Management System (PMS) is essential to dynamically allocate power between the fuel cell and battery with load demand and operating conditions. Thie need for intelligent hybrid power management becomes even more critical in emerging PEMFC with limited thermal margins. In addition to water-cooled PMEFC system widely adopted in heavy-duty truck, air-cooled PEMFC has recently attracted significant attention in lightweight and compact mobility applications due to their simplified balance of plant structure and high power density. However, air-cooled system inherently exhibits narrower thermal margins and stronger coupling between stack temperature, auxiliary power consumption, and dynamic load variations. Therefore, advanced hybrid power management strategies that jointly consider energy efficiency and thermal stability are essential not only for heavy-duty water-cooled system but also for emerging air-cooled PMEFC system.

1.2. Research Survey

Extensive research has been conducted to overcome the inherent limitations of single-stack fuel cell systems by adopting multi-stack configurations. These efforts have focused on improving system efficiency, reliability, and power distribution under varying load conditions. For instance, Han et al. [20] demonstrated through optimized power simulations that activating auxiliary sub-stacks beyond a specific load threshold can significantly enhance overall system efficiency. Similarly, Dépature et al. [21] developed a simulation-based control design framework and confirmed that multi-stack systems outperform single-stack architectures in terms of integration, reliability, and performance. Zhou et al. [22] explored power allocation strategies that account for both efficiency and stack lifetime, while Marx et al. [23] proposed a rule-based energy management framework incorporating a combined index to balance efficiency with long-term durability. In addition to fuel cell–centric studies, parallel research efforts have also focused on energy management strategies for other electrochemical energy storage systems employed in hybrid powertrains.

In parallel with fuel cell–based hybrid powertrains, energy management strategies for other electrochemical energy storage systems, such as lithium-ion batteries and supercapacitors, have also been widely investigated. Battery management systems (BMS) focus on state estimation, charge–discharge control, and lifetime management, with model-based approaches such as extended Kalman filtering and equivalent circuit modeling being commonly adopted for real-time applications [24,25]. Comprehensive surveys further indicate that battery management requirements depend strongly on the assigned role of the battery and system-level integration within the powertrain [26]. Beyond batteries, supercapacitors have attracted attention as auxiliary energy storage devices due to their high power density and fast dynamic response, and hybrid battery–supercapacitor energy management strategies, including model predictive control approaches, have been proposed to mitigate transient load fluctuations and improve overall system performance [27,28].

Thermal management has also been a key research focus, given its critical role in ensuring stack performance, efficiency, and lifespan. Qiu et al. [29] compared series and parallel cooling architectures for multi-stack systems under various load conditions, identifying their respective operational advantages. Shen et al. [30] further highlighted that series configurations provide superior temperature uniformity under steady-state loads, whereas parallel layouts offer improved thermal control during transient operations. Fault detection and control approaches have also been proposed, including Su et al.’s [31] Unscented Kalman Filter-based method for sensor fault diagnosis and Saygili et al.’s [32] ITAE-based controller optimization to minimize parasitic power losses. Additionally, Hu et al. [33] introduced a robust incremental fuzzy control method for cooling components such as pumps and bypass valves, improving system resilience to parameter variations and disturbances.

Parallel to thermal studies, power management strategy (PMS) research for fuel cell electric vehicles (FCEVs) has evolved to address the dual-source power distribution challenge between the fuel cell and battery. Existing PMS approaches can be broadly classified into rule-based control, state-machine-based control, and fuzzy-logic-based control strategies. Rule-based PMS methods have been widely adopted due to their simplicity, transparency, and ease of implementation, and have been shown to provide robust performance under predefined operating conditions [34,35,36]. State-machine-based approaches extend rule-based strategies by introducing explicit operating modes and state transition logic, enabling improved adaptability to varying load and operating conditions [37,38]. More recently, fuzzy-logic-based PMS methods have attracted increasing attention due to their ability to handle nonlinear system behavior and uncertainties without requiring precise mathematical models, offering enhanced performance in terms of energy efficiency and thermal stability [39,40]. In addition to these heuristic-based strategies, optimization-oriented PMS approaches, such as dynamic programming and predictive control, have also been investigated to further improve system-level performance [41,42]. However, these methods often involve higher computational complexity, which may limit their real-time applicability in large-scale vehicle systems.

Despite these advances, most previous studies have concentrated on subsystem-level optimization, such as air supply, hydrogen flow, or thermal regulation, or have evaluated PMS strategies primarily based on state of charge and hydrogen consumption. However, comprehensive vehicle-level evaluations that simultaneously consider multi-stack architecture, thermal management, and power control remain limited. To address this gap, the present study develops a full-vehicle physical model of a hydrogen-powered truck using MATLAB/Simscape™ based on the Hyundai Xcient. The model integrates electrochemical, thermal-fluid, and mechanical domains and implements three distinct power management approaches, rule-based control, state machine control, and fuzzy logic control, which are comparatively evaluated using the TOPSIS method. Furthermore, this comparative evaluation approach may serve as a conceptual basis for future studies on air-cooled PEMFC-based hybrid systems, provided that appropriate system models and parameters are established.

2. System Configuration

A dynamic model of the hydrogen-powered electric truck was constructed based on the specifications of Hyundai’s Xcient heavy-duty truck. The overall system architecture and key parameters are summarized in Figure 1 and

Table 1. Fuel cell system specifications.

System	Components	Parameters	Unit
Power Supply System	Number of Stack	2	-
	Power of Stack	90	kW
	Number of Battery	3	ea
	Power of Battery	24	kWh
Driving System	Power of Motor	350	kW
	Torque of Motor	2237	Nm

. The vehicle powertrain was structured into three primary subsystems:

(1)

Power Source Module: comprising dual PEM fuel cell stacks and high-voltage battery pack, which jointly supply the electrical energy required for propulsion

(2)

Power Conversion Module: including DC-DC converter and inverter that regulate voltage levels and enable efficient energy transfer to the traction motor

(3)

Drivetrain Module: consisting of an electric traction motor coupled with a reduction gear to convert electrical energy into mechanical motion and deliver the required torque to the wheels

Furthermore, a power management system was integrated into the model to coordinate energy distribution between the fuel cell and battery in real time, ensuring stable operation and optimal performance under varying load conditions.

2.1. Fuel Cell System

To meet the high power requirements of heavy-duty applications, the vehicle integrates two PEMFC stacks, each rated at 90 kW, forming a combined primary power source for the propulsion system. To maintain reliable operation, the fuel cell subsystem incorporates a comprehensive Balance of Plant (BoP), which includes hydrogen delivery, air supply, and thermal regulation units. These subsystems ensure stable reactant supply, appropriate humidity control, and precise temperature regulation under dynamic load conditions.

The dynamic fuel cell model developed in this study was constructed under several simplifying assumptions to facilitate system-level simulation and reduce computational complexity:

-

Hydrogen and air flows are modeled as ideal gases with fixed stoichiometric ratios and constant relative humidity.

-

The electrochemical reaction is considered spatially uniform, and concentration gradients within the electrodes are neglected.

-

The temperature field inside the stack is represented using a lumped parameter approach, assuming homogeneous temperature distribution throughout the cell layers.

2.1.1. Fuel Processing System

The vehicle incorporates seven onboard high-pressure hydrogen storage tanks, each capable of storing compressed hydrogen gas at 700 bar. To deliver hydrogen safely to the fuel cell stacks, a two-stage pressure regulation mechanism is implemented [43]. Specifically, the hydrogen supply pressure is reduced in two sequential stages. In the first stage, the pressure is decreased from the storage level of 700 bar to an intermediate pressure of approximately 10 bar, which ensures safe downstream handling and protects system components from excessive mechanical stress. In the second stage, the hydrogen pressure is further regulated from this intermediate level to the operating pressure required at the fuel cell stack inlet, enabling stable and reliable hydrogen supply under varying load conditions. This multi-step pressure reduction process plays a critical role in maintaining system safety and performance, as directly supplying high-pressure hydrogen could lead to mechanical stress, component damage, or rapid degradation of electrochemical performance [44,45,46,47,48]. By stabilizing the supply pressure, the system ensures consistent and efficient fuel cell operation across a wide range of load conditions.

The hydrogen flow rate required for stack operation is primarily governed by the load current. Based on the consumption rate, the remaining hydrogen mass in the storage tanks can be estimated using the following relationship:

$${\dot{m}}_{H_2}={\displaystyle \frac{i}{nF}}\times A\times n_{cell}\times stoi$$

(1)

$$m_{H_2,remain}=m_{H_2,tank}\times n_{tank}-\int {\dot{m}}_{H_2}$$

(2)

Here, ${\dot{m}}_{H_2}$ represents the molar flow rate of hydrogen, $F$ represents the Faraday constant, $i$ is the current density, $A$ is the active area, and $stoi$ denotes the stoichiometric ratio. In addition, $n$ represents the number of electrons involved in the electrochemical reaction, and $n_{cell}$ denotes the number of cells in the fuel cell stack. Where the hydrogen consumption is obtained by time integration of the hydrogen molar flow rate over time $t$.

2.1.2. Air Processing System

Air supply systems for PEMFC stacks can be broadly divided into atmospheric and pressurized types, depending on the required performance characteristics and system design constraints [49,50,51]. In this study, a pressurized supply configuration was selected, employing a centrifugal compressor to provide sufficient oxidant flow for the dual 90 kW-class fuel cell stacks. The adoption of a pressurized air system enables rapid transient response and enhances overall stack efficiency under dynamic load conditions.

Similar to the hydrogen subsystem, the required air flow rate is governed by the load current, ensuring proper stoichiometric balance during operation. The compressor discharge pressure is determined as a function of system efficiency and the specific heat ratio, as expressed by the following relationship:

$${\dot{m}}_{Air}={\displaystyle \frac{i}{nF}}\times A\times n_{cell}\times stoi$$

(3)

$$P={\left(1+{\displaystyle \frac{\eta \left(\dot{m}, U\right)}{T_{atm}{C}_P}}\right)}^{{\displaystyle \frac{\gamma }{(\gamma -1)}}}\times P_{atm}$$

(4)

Here, ${\dot{m}}_{Air}$ represents the molar flow rate of air, $\eta$ is the compressor efficiency, $\gamma$ is the specific heat ratio, and $C_P$ is the specific heat at constant pressure.

2.1.3. Stack

The output voltage of a fuel cell stack is influenced by several electrochemical and transport-related phenomena. The theoretical reversible potential, often referred to as the Open Circuit Voltage (OCV), is reduced in practical operation due to multiple overvoltage losses. These include activation overvoltage associated with charge transfer kinetics, concentration overvoltage caused by mass transport limitations of the reactants, and ohmic overvoltage resulting from resistance to electron and ion conduction within the cell components. The overall cell voltage can therefore be expressed as follows:

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

(5)

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

(6)

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

(7)

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

(8)

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

(9)

Here, n is the number of electrons involved in the electrochemical reaction, p denotes the partial pressure of each reactant, and $\alpha$ is the charge transfer coefficient. In addition, $E$ represents the reversible open-circuit voltage, $V_{act}$, $V_{conc}$, and $V_{ohm}$ denote the activation, concentration and ohmic overvoltages, respectively, and $i_L$ denotes the limiting current density constrained by mass transport limitations.

The fuel cell stack model developed in this study was validated against experimental data reported in previous single-cell studies, as illustrated in Figure 2 [52]. In that study, the PEMFC polarization characteristics were measured under steady-state operating conditions at a cell temperature of 65 °C using a three-cell PEMFC stack with an effective active area of 25 cm² per cell. Humidified hydrogen and air were supplied to the anode and cathode, respectively, and the polarization curve was obtained over a current density range of 0–1.6 A/cm². The stack’s overall performance characteristics were extrapolated from single-cell results by proportionally scaling the specifications with the total number of unit cells. To ensure reliable operation under high-load conditions and to prevent issues such as increased membrane resistance, water and thermal management instability, and the rise of concentration and activation overpotentials, the maximum operating current density was constrained to below 0.8 A/cm² [53,54,55,56,57,58].

2.1.4. Thermal Management System

The thermal management architecture developed for the hydrogen-powered truck is composed of distribution valve, three-way valve, coolant circulation pump, radiator, and cooling fan, as shown in Figure 3. The specifications and configurations of these components were derived from a validated reference model, whose performance had been experimentally verified in a hydrogen fuel cell truck application [59]. Based on this design reference, the same component parameters were adopted in the present system model to ensure realistic thermal behavior and system performance.

Maintaining the fuel cell stacks within an optimal thermal operating range is essential for maximizing electrochemical efficiency and ensuring long-term durability. In particular, stable operation of both Stack 1 and Stack 2 at approximately 343.15 K enhances reaction kinetics, thereby improving overall system performance and extending component lifetime [60,61,62]. To achieve precise thermal regulation under varying load conditions, Proportional Integral (PI) controllers were implemented across multiple subsystems. The cooling fan was controlled to maintain the coolant temperature at point T1 around 333.15 K, while additional PI controllers regulated the distribution valve (T2) and coolant pump (T3) to sustain their respective setpoints near 343.15 K.

The radiator employed in the thermal management system is configured as a louver-fin type heat exchanger. During operation, the coolant absorbs heat generated within the fuel cell stack and is subsequently cooled as thermal energy is transferred to the ambient airflow, which is supplied either by the vehicle’s forward motion (ram air) or by the operation of the cooling fan. The heat exchanger’s thermal performance is quantitatively assessed based on its effectiveness (ε) and Number of Transfer Units (NTU), which are defined as follows:

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

(10)

$$\mathrm{N}\mathrm{T}\mathrm{U}={\displaystyle \frac{1}{c_{min}\times R}}$$

(11)

Here, $c_{min}$ represents the minimum heat capacity rate, and $R$ denotes the thermal resistance component. The heat transfer rate is calculated based on the effectiveness as shown below, while the maximum heat transfer rate is determined by the minimum heat capacity rate and the temperatures of the coolant and air entering the heat exchanger.

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

(12)

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

(13)

Through this heat transfer process, the outlet temperatures of the coolant and air can be calculated as follows:

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

(14)

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

(15)

Although temperature regulation is applied across all sections of the cooling loop, the concurrent operation of multiple controllers at the same control point can result in interaction effects, potentially degrading system stability and control performance [63,64,65]. To mitigate this issue, a simplified rule-based control strategy was implemented for the three way valve in place of a more complex control algorithm. In this scheme, the valve opening ratio is modulated with the fuel cell stack load, thereby enabling stable and responsive thermal control.

2.2. Battery

The battery system in the hydrogen-powered truck functions as a supplementary energy source, playing a crucial role in compensating for the inherently slow dynamic response of the fuel cell system during transient load changes [66,67,68]. In this study, the battery was configured based on the specifications of Hyundai’s Xcient as a high-voltage lithium-ion pack with a total capacity of 72 kWh, comprising three modules of 24 kWh each. The nominal system voltage is 630 V. Operating in a hybrid configuration alongside the fuel cell stacks, the battery ensures continuous and reliable power delivery under varying load conditions, thereby enhancing vehicle responsiveness and overall energy management performance.

2.3. DC-DC Converter

To match the distinct voltage levels of the fuel cell and battery with the requirements of the traction motor, DC–DC power converters were integrated into the system architecture [69,70]. Because the battery undergoes both charging and discharging processes depending on its SoC during vehicle operation, a bidirectional converter is necessary to enable power flow in both directions. Accordingly, a unidirectional converter was assigned to the fuel cell subsystem to step up its output voltage, while a bidirectional converter was incorporated into the battery subsystem to manage bidirectional energy exchange, ensuring proper voltage conversion and seamless hybrid power operation.

2.4. Powertrain System

The powertrain subsystem converts the electrical energy generated by the fuel cell and battery into mechanical power via the traction motor, enabling the vehicle’s propulsion. It delivers the tractive force necessary to counteract various resistive loads that occur during driving, such as gravitational forces from vehicle mass, road incline resistance, and aerodynamic drag. These resistive components are calculated using the following equations, with detailed system parameters summarized in

Table 2. Xcient vehicle specification [9].

System	components	parameters	Unit
Vehicle Specification	Vehicle Mass	28,000	kg
	Tire Rolling Radius	0.286	m
	Tire Rolling Coefficient	0.008	-
	Air Drag Coefficient	1.15	-
	Vehicle Front Area	2.54 × 3.73	m2
	Reduction Gear Ratio	8	-
	Gravitational Acceleration	9.81	m/s2

. Furthermore, because the traction motor inherently operates with low torque and high rotational speed, a reduction gear is integrated into the drivetrain to increase output torque and satisfy the propulsion requirements of the vehicle [71,72,73].

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

(16)

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

(17)

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

(18)

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

(19)

Here, $F_{drive}$ denotes the traction force generated by the motor, which is driven by the power supplied from the hydrogen fuel cell and battery, and is calculated through torque and wheel radius. $F_{brake}$ represents the braking force generated for vehicle deceleration. In addition, $F_{resist}$ represents the total resistive force, which includes rolling resistance caused by tire-road contact, aerodynamic drag caused by air during vehicle motion, and gravitational resistance due to road slope.

2.5. Power Management System

To ensure a fair comparison among the three power management strategies, all PMSs were designed to determine the fuel cell output power based on the same input information, namely the traction load power and battery SoC. The PMSs differ only in the complexity and adaptability of their control logic and are classified into rule-based control, state machine control, and fuzzy logic control strategies. The hydrogen electric truck operates under a hybrid powertrain architecture that integrates hydrogen fuel cell system with a high-voltage battery. Effective coordination between these two energy sources requires well-designed PMS capable of dynamically allocating power with the vehicle’s traction demand. The PMS plays a crucial role in optimizing overall system efficiency while distributing power within the physical and operational constraints of both subsystems [74,75]. In this study, three different PMS strategies—rule-based control, state machine control, and fuzzy logic control—were developed and evaluated.

The rule-based control approach is widely adopted due to its simplicity and ease of implementation, as control decisions are derived from predefined logic rules. However, the static nature of these rules often limits adaptability, making it challenging to maintain optimal performance under rapidly changing or unpredictable operating conditions. State machine control, on the other hand, models system behavior using discrete operational states and transition conditions, providing a structured and easily interpretable control. Yet, as system complexity increases, the number of states can grow significantly, complicating controller design and increasing maintenance efforts. Finally, fuzzy logic control offers high flexibility in managing nonlinear and uncertain environments by using linguistic variables and membership functions. Despite its robustness, the computational cost associated with processing many fuzzy rules can become a limiting factor in real-time applications [76,77,78,79,80,81].

2.5.1. Rule-Based Control PMS

Rule-based control is widely recognized for its simplicity and rapid implementation, as control decisions are derived directly from predefined logical rules [82]. In this study, a rule-based PMS was developed using a conditional if–then structure, with SoC and power demand serving as the primary control variables. SoC thresholds were set at 0.4 and 0.6 to define the system’s operating regions. Based on these thresholds, the control strategy was divided into three distinct operating modes: battery-dominant drive, hybrid drive, and fuel-cell-dominant drive, as illustrated in Figure 4.

In the battery-dominant drive mode, which is activated when the SoC exceeds 0.6, the traction power is supplied primarily by the battery, utilizing its maximum power capability. When the SoC is within the intermediate range (0.4–0.6), the system transitions into the hybrid drive mode, in which both the fuel cell and battery contribute to power delivery. In this mode, the fuel cell stack operates near its rated power to supply the base load, while additional power requirements are met by the battery. Finally, when the SoC drops below 0.4, the system enters the fuel-cell-dominant mode, where the fuel cell becomes the primary power source. Under light-load conditions in this mode, the fuel cell not only supports propulsion but also charges the battery to restore its SoC. In this study, the rule-based control logic was explicitly implemented using a conditional if–then structure to ensure transparency and reproducibility.

(1)

When the battery SoC exceeds 0.6, the fuel cell output power is restricted to its minimum operating level, and the traction power demand is mainly supplied by the battery.

(2)

For intermediate SoC values (0.4 ≤ SoC ≤ 0.6), the fuel cell operates at a predefined base power level to cover the nominal load, while additional power demand is supplemented by the battery, resulting in hybrid operation.

(3)

When the SoC falls below 0.4, battery discharge is limited and the fuel cell is assigned as the primary power source to meet the traction demand; under low-load conditions, surplus fuel cell power is utilized to recharge the battery.

These control rules were consistently applied throughout all simulations, ensuring deterministic behavior and reproducibility of the rule-based PMS.

2.5.2. State Machine Control PMS

State machine control is a supervisory control approach that governs system operation by defining discrete operating states and the conditions under which transitions between them occur. The framework typically consists of four key elements: state, event, transition, and action. Here, a state represents a specific operating condition of the system, while an event acts as a trigger that may initiate a state change. A transition occurs when predefined logical conditions are satisfied in response to an event, leading to an action, which refers to the control operation executed after entering the new state.

In this study, the battery’s SoC was divided into three operating states: Low, Medium, and High. Simultaneously, the traction power demand was segmented into five discrete levels, from $P_{\mathrm{Load},1}$ to $P_{\mathrm{Load},5}$. Based on the combination of these state definitions, the output power of the fuel cell stack was determined with the control logic summarized in

Table 3. State machine control state.

State	SoC [-]	Load Power [kW]	Fuel Cell Power [kW]
1	Low	Pload > P Load,1	Pload + P8
2	Low	Pload > P Load,2	Pload + P6
3	Low	Pload > P Load,3	Pload
4	Low	Pload > P Load,4	Pload − P1
5	Low	Pload > P Load,5	Pload − P2
6	Medium	Pload > P Load,1	Pload
7	Medium	Pload > P Load,2	Pload
8	Medium	Pload > P Load,3	Pload − P6
9	Medium	Pload > P Load,4	Pload − P7
10	Medium	Pload > P Load,5	Pload − P8
11	High	Pload > PLoad,1	Pload
12	High	Pload > P Load,2	Pload
13	High	Pload > P Load,3	Pload − P8
14	High	Pload > P Load,4	Pload − P9
15	High	Pload > P Load,5	Pload − P10

. When the system operates in the Low-SoC state, the controller increases fuel cell power output relative to the load level to compensate for insufficient battery contribution and to initiate recharging. In contrast, during the High-SoC state, the fuel cell output is deliberately reduced, shifting a larger share of the power supply responsibility to the battery and thereby minimizing fuel cell operation. The Medium-SoC state serves as an intermediate condition, where power distribution is dynamically adjusted with real-time load demand and system operating conditions. In the implemented state machine control PMS, the operating logic was explicitly defined by the combination of discrete battery SoC states and load power levels. The battery SoC was classified into three states (Low, Medium, and High), while the traction load power was discretized into five predefined levels. Each unique combination of SoC state and load level corresponds to a specific operating state, in which the fuel cell output power is deterministically assigned according to the predefined state table. State transitions occur when either the SoC crosses its threshold values or the load power moves between adjacent load regions, ensuring predictable and structured control behavior. Through this formulation, the state machine controller provides transparent control logic and guarantees reproducible power allocation across all simulation scenarios.

2.5.3. Fuzzy Logic Control PMS

Fuzzy logic control (FLC) is an intelligent control technique that utilizes fuzzy set theory to provide flexible and robust control, enabling various implementation approaches such as manual tuning, self-learning, and adaptive fuzzy schemes. In this study, the Mamdani-type fuzzy inference model was selected due to its intuitive rule-based structure, simplicity, and broad applicability in complex dynamic systems [83,84,85]. The FLC design consists of three main stages. In the fuzzification stage, crisp input variables are transformed into fuzzy sets through membership function evaluation. The inference stage then applies a series of fuzzy rules to derive control decisions based on predefined linguistic conditions. Finally, the defuzzification stage converts the fuzzy outputs into a precise control signal suitable for system actuation.

As shown in Figure 5, the controller was implemented with two input variables, battery SoC and system load power, and one output variable: the fuel cell stack power. Figure 5a–c illustrate the membership functions for SoC, load power, and fuel cell power output, respectively, while Figure 5d presents the resulting fuzzy rule surface. The SoC variable was divided into five linguistic categories: VL (Very Low), L (Low), M (Medium), H (High), and VH (Very High). System load power was described using seven categories: Off, VS (Very Small), S (Small), M (Medium), L (Large), VL (Very Large), and Max. The fuel cell output was further segmented into nine membership levels, ranging from idle operation to full power.

Because the durability and lifetime of fuel cells are highly sensitive to operational dynamics, particularly frequent start-stop cycles, rapid power fluctuations, and extended high-power operation, the membership functions for fuel cell power were carefully designed to ensure smooth power transitions. To further clarify the implementation of the fuzzy logic controller, representative fuzzy rules are summarized as follows. When the battery SoC is Very Low (VL) and the load power is Large or Very Large, the fuel cell output is set to a High or Very High level to ensure sufficient traction power while preventing excessive battery discharge. When the SoC is Medium and the load power is Medium, the fuel cell operates at a Medium power level, resulting in balanced hybrid operation between the fuel cell and battery. In contrast, when the SoC is High or Very High and the load power is Small, the fuel cell output is reduced to a Low level, allowing the battery to supply a larger portion of the traction demand. These fuzzy rules collectively enable continuous and nonlinear power allocation, avoiding abrupt switching behavior and ensuring smooth fuel cell power trajectories across varying load and SoC conditions. By preventing abrupt output changes, the fuzzy controller minimizes degradation mechanisms and significantly improves the long-term stability of the fuel cell system [86].

3. Results and Discussion

The performance of the proposed hydrogen electric truck model was assessed using an identical driving cycle designed to reflect representative Korean road gradients and real-world driving patterns. For each power management strategy, power distribution between the fuel cell and battery was determined with the corresponding control logic. The resulting system behavior was then comparatively evaluated across key performance indicators, including battery SoC variation, hydrogen consumption, temperature overshoot characteristics, and parasitic energy demand.

3.1. Simulation Scenario

To evaluate the performance of the proposed PMS strategies, a representative driving cycle was designed based on road gradient data from previous studies conducted in Korea, as shown in Figure 6 [87,88]. The vehicle’s speed profile spans a range of 0–80 km/h, incorporating an acceleration period of approximately 1600 s followed by a deceleration phase lasting 900 s. Throughout the driving cycle, the vehicle maintains an average velocity of about 58.7 km/h. Additionally, a steady-speed segment was included between 6800 and 8200 s, during which the vehicle operates at a constant 70 km/h. This portion of the test cycle was specifically designed to investigate the influence of road gradients on traction power demand. Incline variations of −3°, 0°, and +3° were introduced to examine power fluctuation and system response under different slope conditions.

3.2. Vehicle Performance

The dynamic performance of the hydrogen electric truck under the designed Korean driving cycle is presented in Figure 7. As illustrated, the vehicle closely follows the prescribed speed and road gradient profiles, demonstrating reliable speed-tracking capability throughout the simulation. Minor deviations occur during transitions in the target velocity profile, with the maximum speed error of approximately 0.3 km/h observed near 200 s in the initial acceleration phase. This deviation results from the rapid increase in motor and gearbox torque required to overcome the vehicle’s inertial resistance when accelerating from rest.

During both the start-up phase and subsequent speed transitions, the control system actively modulates torque to minimize tracking error. Once steady-state operation is achieved, torque demand stabilizes under constant-speed conditions. Notably, within the 6800–8200 s interval, which corresponds to sustained operation at 70 km/h, a noticeable rise in torque demand occurs between 7200 and 7700 s due to the uphill segment. Conversely, as the vehicle transitions to a downhill slope from 7700 to 8200 s, the required torque decreases because the gravitational component assists vehicle propulsion.

Vehicle transitions to a downhill slope from 7700 to 8200 s, the required torque decreases because the gravitational component assists vehicle propulsion.

3.3. Power Management System Comparison

The PMS plays a crucial role in determining both the energy efficiency and operational stability of a hydrogen electric truck. Therefore, this study performed a comparative evaluation of system performance under identical initial SoC and driving conditions, with a particular emphasis on assessing the influence of different PMS approaches.

3.3.1. Result of Rule-Based Control PMS

Figure 8 presents the power distribution profile and the evolution of battery SoC under the rule-based control PMS strategy. At the beginning of the driving cycle, the vehicle operates exclusively on battery power since the traction demand is relatively low, and the fuel cell remains inactive. As the load gradually increases, the fuel cell is activated at around 500 s and assumes the role of the primary power source, while the battery supplements the system by compensating for transient power deficits, enabling hybrid operation. Once the SoC falls below 0.4 at approximately 5700 s, the battery ceases to discharge, and the fuel cell becomes the sole source of traction power under high-load conditions. As the power demand decreases later in the cycle, the fuel cell not only continues to meet a portion of the propulsion requirements but also contributes to recharging the battery. Over time, the SoC recovers to a stable level, and the system reaches a steady operating state. The simulation results demonstrate that the rule-based control PMS maintains a relatively stable power-sharing behavior by adjusting the power contribution from the fuel cell and battery with predefined control logic. However, fluctuations in load demand and SoC occasionally led to abrupt variations in the power output of both energy sources, highlighting a limitation of the rule-based control approach.

3.3.2. Result of State Machine Control PMS

Figure 9 illustrates the operational characteristics of the power management system governed by state machine control, where operating modes are determined by the combination of battery SoC and load power conditions. During the initial 1000 s of the driving cycle, the SoC remains within the medium range and the power demand is relatively low, resulting in fuel cell–only operation (State 5). As the load gradually increases, the control logic transitions the system into hybrid operation modes (States 8 and 9), where both the fuel cell and battery contribute to power delivery.

Beyond approximately 4000 s, the battery SoC drops below 0.4, limiting the battery’s contribution and causing the fuel cell to supply most of the propulsion power under high-demand conditions (State 4). After 8200 s, during the reduced-load phase, the fuel cell continues to operate at elevated output levels to facilitate battery recharging (States 1 and 2). Because the state machine framework operates according to explicitly defined conditions and state transitions, it offers predictable and structured system behavior. However, one drawback of this approach is the occurrence of sudden power fluctuations during state changes, which can reduce control flexibility when responding to rapidly varying load conditions.

3.3.3. Result of Fuzzy Logic Control PMS

Figure 10 shows the power management behavior of the FLC strategy, where fuel cell output is determined through fuzzy inference using the membership functions of battery SoC and load power. At the beginning of the driving cycle, when the SoC is around 0.6, the system primarily falls within the Medium and Low membership regions. During this stage, the fuel cell operates under low-load conditions to support battery charging. As the load demand increases and the SoC gradually decreases between 3000 and 6500 s, the control system transitions into the Medium operating region, resulting in hybrid operation with both the fuel cell and battery supplying power. Under conditions classified as Very Large load, the battery delivers a greater share of the total power demand to support peak loads. After 8000 s, as the load decreases while the SoC remains low, the fuel cell once again ramps up its power output to recharge the battery. The fuzzy logic control PMS exhibits nonlinear and continuous control characteristics, allowing adaptive adjustment of fuel cell output in response to dynamic changes in SoC and load. This continuous control behavior reduces abrupt load fluctuations on the fuel cell, thereby minimizing parasitic energy losses and mitigating transient response effects across the system.

3.4. Thermal Management System Comparison

Depending on the power required from the fuel cell, the corresponding heat generation varies depending on the applied PMS of the hydrogen electric truck. However, to prevent performance degradation, such as material damage, electrolyte dehydration, and thermal deformation, the operating temperature of the fuel cell stack is typically maintained within the range of 333.15 K to 353.15 K [89,90,91,92]. Accordingly, in this study, the inlet and outlet temperatures of the fuel cell coolant were controlled to 333.15 K and 343.15 K, respectively. The resulting temperature profiles under each PMS are shown in Figure 11. In the rule-based control and state machine control cases, the fuel cell experienced abrupt high-power operation at approximately 5800 s and 4000 s, respectively. In contrast, the fuzzy logic control strategy mitigated such high load operation by effectively utilizing the battery as an auxiliary power source during the 3000–6400 s interval, thereby distributing the fuel cell load and preventing thermal stress on the stack.

To quantitatively evaluate temperature regulation performance across different PMS strategies, temperature overshoot was calculated as defined in Equation (20).

$$Overshoot=\left({\displaystyle \frac{T_{Max}-T_{Target}}{T_{Target}}}\right)\times 100$$

(20)

The calculated temperature overshoot at the inlet of Stack 1 was 1.51% under the rule-based control PMS, 1.71% under state machine control, and 1.22% when using fuzzy logic control. At the outlet of Stack 2, the corresponding overshoot values were 3.84%, 2.69%, and 1.49%, respectively. These results demonstrate that the fuzzy logic control significantly reduced temperature fluctuations compared with the other control methods, achieving up to 28.65% lower overshoot at the Stack 1 inlet and as much as 61.20% reduction at the Stack 2 outlet. The superior thermal management performance of the fuzzy logic–based PMS is primarily attributed to its ability to mitigate rapid variations in fuel cell load, thereby suppressing abrupt changes in heat generation. Since the heat generation of the fuel cell stack is directly coupled with variations in output power and current, stepwise changes in fuel cell output—such as those observed in the rule-based and state machine–based PMSs—can lead to pronounced temperature overshoot due to the inherent response delay of the cooling system. In contrast, the fuzzy logic–based PMS processes the battery SoC and traction load power as continuous fuzzy sets, enabling gradual and nonlinear modulation of the fuel cell output power. This allows the battery to be actively utilized as an auxiliary power source under high-load conditions, limiting sudden increases in fuel cell output and maintaining the cooling system within a more stable operating range. As a result, the temperature overshoot is effectively reduced. Such improvements in temperature stability can mitigate the occurrence of local hotspots and repetitive thermal stress, which are known to accelerate catalyst degradation, membrane–electrode assembly (MEA) damage, and long-term performance degradation of fuel cell stacks. From this perspective, the enhanced thermal stability achieved by the fuzzy logic–based PMS suggests a positive potential impact not only on short-term thermal management performance but also on the long-term reliability and durability of the fuel cell system [93]. The enhanced thermal performance is primarily due to the fuzzy controller’s capability to smooth out rapid load transitions through gradual and adaptive power allocation. This mitigates sudden temperature spikes within the stack, thereby improving thermal stability and overall system reliability. Beyond short-term thermal stability, temperature regulation is closely related to the long-term durability of PEMFC stacks. Previous experimental studies have demonstrated that temperature fluctuations and local thermal non-uniformity accelerate catalyst degradation, membrane mechanical stress, and overall performance decay [94]. From this perspective, the reduced temperature overshoot and enhanced thermal stability achieved by the fuzzy-logic-based PMS in this study are expected to mitigate thermally induced degradation mechanisms. Although the present model assumes a spatially uniform stack temperature, the observed improvement in thermal stability indicates a favorable operating condition for long-term fuel cell reliability.

3.5. Hydrogen Consumption

The hydrogen consumption of a PEMFC system is directly related to the load current supplied to the stack, and its relationship can be described by the current-based formulation shown below.

$${Consumption}_{H_2}=\int {\displaystyle \frac{i}{nF}}\times stoi\times n_{cell}\times M$$

(21)

where the hydrogen consumption is obtained by time integration of the hydrogen molar flow rate over time $t$. In addition, ${Consumption}_{H_2}$ represents the total hydrogen consumption, $n_{cell}$ is the number of fuel cell in the stack, and $M$ is the molar mass of hydrogen. The calculation results are presented in

Table 4. Hydrogen consumption with PMS.

	Rule-Based Control PMS	State Machine Control PMS	Fuzzy Logic Control PMS
Hydrogen Consumption [kg]	17.1689	16.7903	16.6408

. Consequently, the higher power demand supplied by the fuel cell under the rule-based and state machine strategies led to increased hydrogen consumption relative to the fuzzy logic approach. The fuzzy logic controller achieved reductions of approximately 3.08% and 0.89% in hydrogen usage compared with the rule-based and state machine controls, respectively. This enhanced performance is mainly due to the optimized fuzzification process, which enables smoother power regulation and minimizes high-load operation and rapid power fluctuations in the fuel cell stack.

3.6. Parasitic Power

Stable power generation in a fuel cell system requires the reliable operation of BoP components, including the air compressor for oxidant supply as well as the coolant pump and cooling fan for thermal regulation. However, a portion of the power produced by the fuel cell is inevitably consumed by these auxiliary devices, known as parasitic power, which reduces the net output available for propulsion and external loads. Thus, minimizing BoP-related power consumption is a key consideration for enhancing overall system efficiency.

In this study, the parasitic power consumption of BoP components—namely the air compressor, coolant pump, and cooling fan—was quantitatively compared over time under three different power management strategies, and the results are summarized in

Table 5. Parasitic energy consumption of BoP components under different power management system.

Time [sec]	Rule-Based Control PMS			State Machine Control PMS			Fuzzy Logic Control PMS
	Cooling Fan [kJ]	Coolant Pump [kJ]	Compressor [kJ]	Cooling Fan [kJ]	Coolant Pump [kJ]	Compressor [kJ]	Cooling Fan [kJ]	Coolant Pump [kJ]	Compressor [kJ]
2000	3.12	5.35	1333.50	2.79	5.45	949.83	2.79	5.43	1179.37
4000	7.61	11.17	4402.93	5.53	11.08	3464.57	7.46	15.27	4358.94
6000	12.96	19.00	7809.13	13.65	47.56	7473.48	14.24	34.89	7876.31
8000	20.84	51.39	11,337.96	102.36	86.68	10,757.89	19.20	47.18	10,754.21
10,000	25.26	59.10	13,042.95	106.86	94.16	12,409.59	23.17	52.53	12,116.30

As shown in the table, the air compressor accountss for the largest share of total parasitic power consumption under all operating conditions, followed by the coolant pump and the cooling fan. For the rule-based control PMS, the parasitic power consumption of the air compressor and coolant pump increases steadily with operating time. This behavior is mainly attributed to the stepwise variations in fuel cell load, which induce abrupt changes in the required air flow rate and coolant flow rate. In particular, as the system enters high-load operating regions, the compressor power consumption increases significantly and becomes the dominant contributor to the overall parasitic power. In the state machine–based PMS, the increase in parasitic power is partially mitigated during certain operating intervals; however, relatively high power consumption of the air compressor and coolant pump is still observed due to output variations associated with state transitions. In contrast, the fuzzy logic–based PMS maintains the lowest power consumption of the air compressor and coolant pump under identical operating conditions, while variations in cooling fan power remain comparatively limited. These results indicate that the fuzzy logic–based PMS effectively reduces parasitic power consumption of key BoP components, particularly the air compressor and coolant pump. As a result, the total parasitic power consumption is reduced by 7.12% compared with the rule-based PMS and by 3.32% relative to the state machine–based PMS.

3.7. Discussion

Although the performance of each PMS was individually assessed in terms of SoC regulation, thermal management effectiveness, hydrogen consumption, and parasitic power losses, such an approach is insufficient to determine the most suitable strategy from a holistic perspective. To address this limitation, the TOPSIS method, which is widely used in multi-criteria decision making, was employed. The method ranks each alternative based on its closeness to the Positive Ideal Solution (PIS), which represents the best achievable performance for each criterion, and its distance from the Negative Ideal Solution (NIS), which corresponds to the worst performance.

(1)

Decision Matrix Construction: A decision matrix is formulated by compiling performance data for each power management strategy based on the selected evaluation criteria.

(2)

Normalization: Each element of the matrix is normalized by dividing it by the vector norm of its respective column to eliminate the influence of units and ensure comparability across criteria.

$$r_{ij}={\displaystyle \frac{x_{ij}}{\sqrt{{\sum }_{i=1}^m{x}_{ij}^2}}}$$

(22)

(3)

Weight Assignment: The weights of the evaluation criteria were objectively determined using a standard deviation–based weighting method, assuming that criteria with larger variability among PMS strategies have greater influence on the decision-making process.

(4)

PIS/NIS Determination: For each performance indicator, the Positive Ideal Solution (PIS) and Negative Ideal Solution (NIS) are identified, corresponding to the best and worst attainable values, respectively.

(5)

Distance and Closeness Coefficient Calculation: The Euclidean distances between each alternative and the PIS/NIS are computed, and the closeness coefficient is derived to rank the alternatives based on their overall performance.

$$S_i^{+}=\sqrt{{\sum }_{j=1}^n{(v_{ij}-v_j^{+})}^2}$$

(23)

$$S_i^{-}=\sqrt{{\sum }_{j=1}^n{(v_{ij}-v_j^{-})}^2}$$

(24)

$$C_i={\displaystyle \frac{S_i^{-}}{S_i^{+}-S_i^{-}}}$$

(25)

In the first step of the analysis, a decision matrix was formulated using multiple performance indicators, such as SoC, thermal regulation effectiveness, hydrogen consumption, and parasitic power usage, as shown below.

$$X=\left[\begin{array}{c}0.5\\ 0.4\\ 0.4\end{array} \begin{array}{c}1.51\\ 1.71\\ 1.22\end{array} \begin{array}{c}3.84\\ 2.69\\ 1.49\end{array} \begin{array}{c}17.17\\ 16.79\\ 16.64\end{array} \begin{array}{c}\mathrm{13,127.31}\\ \mathrm{12,610.61}\\ \mathrm{12,192.01}\end{array}\right]$$

(26)

In the next step, the decision matrix was transformed into a normalized form with Equation (26) to eliminate the influence of differing units among the criteria and enable direct comparison across all performance indicators.

$$R=\left[\begin{array}{c}0.6657\\ 0.5309\\ 0.5244\end{array} \begin{array}{c}0.5837\\ 0.6610\\ 0.4716\end{array} \begin{array}{c}0.7806\\ 0.5468\\ 0.3029\end{array} \begin{array}{c}0.5876\\ 0.5747\\ 0.5696\end{array} \begin{array}{c}0.5992\\ 0.5756\\ 0.5565\end{array}\right]$$

(27)

The weighting factors for the normalized matrix were assigned by considering the variability of each criterion, under the assumption that indicators exhibiting larger fluctuations exert a greater influence on the decision-making process. Consequently, the weights were derived from the standard deviation values of the evaluation criteria. This method assigns higher weights to criteria exhibiting larger performance variations among the PMS strategies, thereby reflecting their greater discriminatory power in the multi-criteria decision-making process. By avoiding subjective judgment or expert scoring, the adopted weighting approach enhances the transparency, objectivity, and reproducibility of the TOPSIS analysis.

$$\omega =\left[0.1794 0.2142 0.5373 0.0210 0.0481\right]$$

(28)

By applying the calculated weights to the normalized matrix, the Euclidean distances to both the PIS and NIS were derived, and the corresponding closeness coefficients were obtained, as summarized in

Table 6. Closeness coefficient of PMS with TOPSIS analysis.

	Rule-Based Control PMS	State Machine Control PMS	Fuzzy Logic Control PMS
Distance to Ideal Solution ($S^{+}$)	0.2578	0.1393	0.0253
Distance to Negative Ideal Solution ($S^{-}$)	0.0303	0.1256	0.2599
Closeness Coefficient ($C$)	0.1051	0.4741	0.9112

. The analysis revealed that the fuzzy logic control strategy achieved the highest closeness coefficient, indicating superior overall performance. The state machine control and rule-based control strategies followed, ranking second and third, respectively.

4. Conclusions

In this study, a hydrogen electric truck equipped with a multi-stack fuel cell system was modeled and analyzed under different power management strategies. Three types of PMS approaches—rule-based control, state machine control, and fuzzy logic control—were developed and evaluated with respect to vehicle dynamics and driving conditions, including road gradient variations. The key findings can be summarized as follows:

(1)

A dual 90 kW fuel cell configuration was implemented alongside essential BoP subsystems, including hydrogen and air supply units, a thermal management circuit, and auxiliary components such as a high-voltage battery, DC/DC converter, and drive motor model.

(2)

For thermal regulation, a series-parallel cooling architecture integrating a distribution valve, coolant pump, three-way valve, radiator, and cooling fan was designed. PI controllers were applied to maintain target temperatures at each critical location.

(3)

To manage power flow in the hybrid fuel cell–battery system, three PMS strategies were implemented: a rule-based method based on load demand and SoC, a state machine controller with discretized operational modes, and a fuzzy logic controller capable of adaptive load distribution via membership functions.

(4)

The fuzzy logic PMS demonstrated the most effective load balancing by utilizing the battery as an auxiliary source during high-power demand periods, thereby alleviating sudden load transients on the fuel cell. As a result, hydrogen consumption decreased by 3.08% and 0.89% compared to rule-based and state machine control, respectively. Parasitic power consumption was reduced by 7.12% and 3.32%, and temperature overshoot was minimized by up to 61.20%.

(5)

Finally, multi-criteria decision analysis using the TOPSIS method confirmed that the fuzzy logic strategy achieved the highest closeness coefficient (0.9112), demonstrating superior overall performance in terms of energy efficiency, thermal stability, and hydrogen utilization. The proposed PMS comparison and TOPSIS-based decision methodology provides a system-level evaluation method that may be extended to other PEMFC architectures, such as air-cooled systems, in future work, subject to dedicated modeling and experimental validation.

(6)

In addition to improving instantaneous thermal performance, the enhanced temperature stability achieved by the fuzzy-logic-based PMS is expected to contribute positively to the long-term durability of PEMFC stacks by mitigating thermally induced degradation mechanisms, highlighting the importance of durability-aware thermal and power management strategies for next-generation hydrogen power systems.