Analysis of the Efficiency of Hydrogen Fuel Cell Vehicle (HFCV) Applications in Manufacturing Processes Using Computer Simulation

Abstract

Implementing innovative solutions in the internal transport of manufacturing enterprises is becoming an important element of improving operational efficiency and reducing greenhouse gas emissions. This article assesses the potential of hydrogen fuel cell (HFCV) forklifts in a steel products manufacturing plant. The verification was carried out using a computer simulation, which enabled the comparison of electric, combustion, and HFCV fleets under identical logistical conditions. The results showed that the HFCV fleet allowed for shorter process execution times and higher utilization compared to electric and combustion variants, mainly due to the elimination of charging and refueling interruptions. Additionally, when powered by green hydrogen, the HFCV fleet offered clear environmental benefits and lower operating costs. The study confirms that HFCV technology can improve the efficiency of internal transport and reduce energy-related operating expenses, although the costs of hydrogen refueling infrastructure were not included and should be addressed in future research.

1. Introduction

In the literature on this subject, increasing emphasis is placed on the importance of logistics as one of the factors influencing economic growth [1,2,3]. The effective functioning of transport systems constitutes a key element in the implementation of the strategic objectives of enterprises, while internal logistics in production plants play a vital role in optimizing production processes and the flow of materials. Consequently, numerous scientific studies have been devoted to the optimization of logistics processes using various mathematical models and heuristic algorithms [4,5,6,7,8,9,10].

Within the framework of Industry 4.0, contemporary logistics challenges are increasingly addressed through modern technologies that enable more efficient supply chain management [11,12,13]. In this context, digital twins have proven particularly useful, as they allow for comprehensive analysis of production and logistics processes from various perspectives, supporting decision-making and process optimization [14,15,16].

A digital twin is defined as a virtual model of a real object, process, or system, continuously updated based on incoming data. It enables system monitoring, behavioral analysis, and simulation under conditions closely resembling reality. The creation of a digital twin involves integrating sensor data with analytical methods, mathematical models, and simulation tools, which not only provides an accurate representation of the current system state but also allows forecasting of potential events and system responses [17,18].

In the context of Industry 4.0 development, the issue of environmental protection is gaining particular importance, as it represents one of the key pillars of contemporary digital transformation [19,20,21]. The literature on the subject extensively analyzes logistics and production systems in terms of their compliance with the principles of sustainable development and their potential to minimize the negative environmental impacts of industrial processes [22,23,24,25]. In this regard, eco-friendly solutions in internal logistics, such as electric forklifts or autonomous guided vehicle (AGV) systems, are becoming increasingly significant, as they allow for reductions in greenhouse gas emissions and electricity consumption [26,27,28].

One of the most promising directions in this field is the implementation of hydrogen technologies, which can contribute to a significant reduction in CO₂ emissions and serve as an alternative to conventional energy sources [29,30,31].

Hydrogen, as a universal energy carrier, may exist in various forms, classified depending on the production methods applied and the potential areas of use [32] (

Table 1. Characteristics of selected hydrogen types.

Hydrogen Type	Description
Gray hydrogen	Produced mainly from fossil fuels such as natural gas or coal, most commonly through steam methane reforming (SMR). This is currently the dominant hydrogen production technology; however, it is associated with high CO2 emissions, which makes it unfavorable from a climate protection perspective [33,34].
Blue hydrogen	Also based on fossil fuels and the reforming process, but supplemented with a stage of carbon capture and storage (CCS). As a result, part of the CO2 emissions does not enter the atmosphere, helping to reduce the carbon footprint of hydrogen production [35].
Green hydrogen	Generated by water electrolysis powered by renewable energy sources such as wind or solar. This process splits H2O molecules into hydrogen and oxygen without producing direct CO2 emissions. It is considered the most sustainable method of hydrogen production [36].
Brown hydrogen	Obtained from coal through gasification or other methods. It is regarded as the most emission-intensive form of hydrogen production because coal has a high elemental carbon content, and hydrogen generation in this process involves significant CO2 emissions [37].
Purple hydrogen	Produced using nuclear energy, typically through high-temperature electrolysis (HTE). The heat generated in nuclear reactors enables hydrogen production without direct CO2 emissions [37].

).

Among all types of hydrogen, particular importance is attributed to so-called green hydrogen, which is considered the most environmentally friendly and sustainable. Its production relies exclusively on the use of renewable sources such as wind, solar, or hydropower, which eliminates greenhouse gas emissions throughout the entire manufacturing process. The alternative is the so-called blue hydrogen, whose production is based on traditional reforming methods; however, through the application of carbon capture and storage (CCS) technology, it allows for a significant reduction in emissions associated with its generation [32].

In the literature, the potential of solutions based on hydrogen transport in industrial and manufacturing processes is increasingly emphasized. Research in this field points to the possibility of using it as an effective alternative to traditional energy sources (

Table 2. Research regarding the application of hydrogen solutions within transport in manufacturing processes.

Author/Authors/Year	Title	Scope of Research
Xiong, Z.; Zhou, H.; Wu, X.; Chan, S.H.; Xie, Z.; Dang, D. (2022) [38]	Work Efficiency and Economic Efficiency of Actual Driving Test of Proton Exchange Membrane Fuel Cell Forklift	Methods: Conducted real-world driving tests comparing a PEM fuel cell forklift with battery-powered and diesel forklifts. Findings: PEM fuel cell forklifts showed higher operational performance and lower running costs, indicating promising potential for practical deployment.
You, Z.; Wang, L.; Han, Y.; Zare, F. (2018) [39]	System Design and Energy Management for a Fuel Cell/Battery Hybrid Forklift	Methods: Developed and tested a hybrid forklift combining fuel cell and battery power, with a dedicated strategy for energy management. Findings: The hybrid system allowed stable operation under various load conditions, ensured efficient energy distribution, and extended the duration of uninterrupted forklift operation.
Yazdi, M.; Moradi, R.; Pirbalouti, R.G.; Zarei, E.; Li, H. (2023) [40]	Enabling Safe and Sustainable Hydrogen Mobility: Circular Economy-Driven Management of Hydrogen Vehicle Safety	Methods: Applied quantitative risk assessment and DEMATEL methods to investigate safety factors in hydrogen vehicle deployment. Findings: Key risk factors and safety measures were identified, supporting safer use of hydrogen vehicles and contributing to sustainable mobility practices.
Radica, G.; Tolj, I.; Lototskyy, M.V. (2024) [41]	Air Mass Flow and Pressure Optimization of a PEM Fuel Cell Hybrid System for a Forklift Application.	Methods: Evaluated the influence of air compressor settings (air flow and pressure) on PEM fuel cell performance in forklifts, using simulations and experiments across different load cycles. Findings: Adjusting air flow and pressure improved system efficiency and operational stability of the fuel cell unit.
Hassan, Q.; Azzawi, I.D.J.; Sameen, A.Z. Salman, H.M. (2023) [42]	Hydrogen Fuel Cell Vehicles: Opportunities and Challenges	Methods: Comprehensive literature review of HFCV technology, infrastructure, and market prospects. Findings: Hydrogen fuel cell vehicles offer environmental and energy benefits, though adoption is limited by technical and infrastructure challenges; future innovations and policies are needed for wider implementation.

).

Building on the overview of hydrogen types and their potential applications in industrial and manufacturing processes, it is worth highlighting one of the most promising practical implementations of hydrogen technology: hydrogen fuel cell-powered forklifts (HFCVs—Hydrogen Fuel Cell Vehicles). These vehicles represent a concrete example of how hydrogen solutions can be integrated into internal transport and logistics operations, translating the environmental and operational advantages of hydrogen into real-world industrial settings.

An increasing number of global manufacturers are introducing innovative solutions in the field of HFCV forklifts. The development of this technology responds to growing market demands related to efficiency, environmental sustainability, and the reliability of logistics equipment [43,44]. In the coming years, this may lead to a significant increase in the popularity of hydrogen-powered forklifts compared to traditional solutions such as forklifts powered by gas, diesel fuel, or electricity.

Of particular note is the fact that HFCV forklifts combine the advantages of electric vehicles with the convenience and speed of refueling typical of combustion machines. As a result, they can represent an attractive alternative both for large logistics centers and for manufacturing companies, where continuity of operations and minimizing downtime are crucial. Moreover, the use of hydrogen as fuel aligns with the global trend of decarbonization and the pursuit of more sustainable energy sources, further strengthening the competitiveness of this technology compared to others [45].

In the face of increasingly strict environmental regulations and growing pressure to reduce CO₂ emissions, manufacturers are investing in the development of hydrogen technologies, and their solutions are gaining increasing recognition in the industry. As a result, it can be expected that in the coming years HFCV forklifts will not only complement but, in many cases, may even replace traditional drives, becoming a new standard in logistics and internal transport.

The aim of this article is to conduct a comprehensive assessment of the effectiveness of implementing internal inter-operational transport based on hydrogen fuel cell (HFCV) forklifts in a production environment. The analysis focuses on economic and ecological aspects, particularly on the potential for reducing carbon dioxide emissions and the impact of the applied solution on the parameters of internal transport processes. The verification of the effectiveness of the proposed solution is carried out in the context of sustainable development, enabling an evaluation of the real potential of HFCV technology in industrial transformation and internal transport optimization.

The study was conducted based on a case study of a real logistics process, in which different forklift fleet scenarios were compared, taking into account both the number of vehicles and the type of drive. The verification includes an analysis of transport execution time, operating costs, and emission levels depending on the fleet configuration. To carry out the research, discrete-event computer simulation was applied, allowing for both quantitative and qualitative assessment of the impact of HFCV implementation on transport processes and the identification of the most efficient operational scenarios. In this study, the digital twin concept was implemented in a simulation model that faithfully replicates the actual internal transport process in the analyzed manufacturing facility. The model provides a digital representation of the system under study, allowing for analysis and operational improvement without interfering with actual operational processes. Simulation allows for comparative experiments with various forklift fleet configurations (including hydrogen-powered vehicles), as well as assessing their impact on operating costs, CO₂ emissions, and internal transport efficiency. This approach is consistent with the digital twin concept, which involves creating digital representations of physical systems to support decision-making and optimize operations in line with Industry 4.0.

In the literature, many studies analyze the environmental dimension of hydrogen utilization, often comparing it with other energy sources and technologies. Their findings show that the effectiveness of hydrogen in reducing greenhouse gas emissions and improving the ecological footprint of transport and energy systems depends on numerous factors, including the production pathway of hydrogen and the origin of the electricity used [45,46,47,48]. For example, publication [45] focused on the possibility of reducing CO₂ emissions through hydrogen co-combustion in aviation engines. In turn, publication [46] underlined that comparisons between hydrogen-based and other energy sources depend strongly on various factors, such as the origin of electricity and the technologies applied for hydrogen production. Furthermore, in publication [49], the performance of hydrogen utilization was analyzed in relation to the type of hydrogen propulsion system applied, indicating differences in efficiency depending on the adopted technology.

The publications also cover other topics, e.g., in ref. [50], aspects related to the safety of using forklift trucks with hydrogen fuel cells were described.

However, these analyses are usually limited either to single criteria (such as emissions or fuel efficiency) or to selected transport sectors, most often passenger cars, heavy-duty vehicles, or aviation. There is still a noticeable lack of research devoted specifically to hydrogen fuel cell forklifts in industrial internal transport. In particular, comprehensive studies combining ecological, economic, and operational perspectives are missing. No works were identified that evaluate fuel cell forklift fleets in real production environments while simultaneously considering operating costs, CO₂ emissions, and process execution time. Moreover, the application of discrete-event simulation as a method for reflecting realistic fleet operation scenarios is underexplored in this context. Addressing this gap is essential for a reliable assessment of the practical potential of HFCV technology in manufacturing plants.

2. Materials and Methods

In this study, an analysis was carried out of the logistics process implemented in a manufacturing plant specializing in the production of steel products. The production process and the related internal logistics activities were carried out within four independent halls, comprising: the input materials warehouse (1), the finished goods warehouse (2), and two production halls ((3) and (4)). Additionally, other infrastructure elements were located within the plant premises, including transport infrastructure (5). The total length of the transport routes within the plant amounts to 492 m (Figure 1).

The production process of the analyzed plant is carried out in a three-shift system, which ensures continuity of manufacturing and optimal utilization of available resources. A crucial role within the process is played by transport operations, which involve the movement of materials between designated pick-up points and distribution locations. These tasks are performed by a forklift fleet with different types of drive systems, including both electric and combustion engine (diesel) vehicles. In total, the plant operates 13 transport units, of which 7 are diesel-powered and 6 are electric-powered (

Table 3. Parameters of electric-powered and diesel-powered forklifts.

Parameter	Electric-Powered Forklift	Combustion Engine (Diesel) Forklift
Maximum load capacity	2 t	2.5 t
Driving speed	10 km/h	10 km/h
Battery capacity	80 V, 500–700 Ah, 42 kWh	-
Battery charging time	120 min	-
Fuel tank capacity	-	45 l
Operation scope/time	6.5 h	18 h

).

Achieving the stated objective, namely the evaluation of the effectiveness of implementing internal inter-operational transport based on hydrogen fuel cell (HFCV) forklifts, was made possible through the development of a four-stage research methodology (Figure 2).

The first stage of the research methodology involved the analysis of parameters generated by the evaluated logistics process. To obtain these parameters, a computer simulation was applied, which made it possible to determine in detail the process execution time, the total distance traveled by internal transport vehicles, and their utilization rate.

The simulation was carried out using FlexSim software, version 22.2.1. Among the many tools available for supporting the analysis of production and logistics processes, FlexSim was chosen due to its high functionality in modeling dynamic material flow systems, its intuitive interface enabling the reproduction of real layouts, and its extensive result visualization capabilities. An additional factor supporting its use was the availability of a research license. The literature widely describes the application of FlexSim as an effective tool for analyzing and optimizing logistics processes, which further confirms the validity of selecting this software for the present study [51,52].

Based on the data obtained from the conducted simulation, Equations (1) and (2) were applied to determine the level of CO₂ emissions generated by the internal transport vehicles.

$$E_1=\sum I \ast T \ast F$$

(1)

$$E_2=\sum C \ast T \ast O$$

(2)

where

E₁—CO₂ emission level generated by electric forklifts.

E₂—CO₂ emission level generated by combustion engine forklifts.

I—electricity consumption level of electric forklifts [kWh/h].

C—fuel consumption of combustion engine forklifts [L/h].

T—operating time of forklifts.

F—CO₂ emission factor for 1 kWh of electricity.

O—CO₂ emission factor for 1 L of used fuel.

F—Carbon dioxide emission factor per unit of electricity (F), determined based on the source of energy supplied to the charging system. Based on the 2024 report of the National Center for Emissions Balancing and Management (KOBiZE), the EI factor for Poland is 0.597 kg CO₂ per each kilowatt-hour (kWh) produced.

O—The CO₂ emission factor for diesel is approximately 2.68 kg CO₂ per liter (O). This value is based on data from the International Energy Agency (IEA).

Next, based on the collected process data, the costs related to the operation of electric and combustion forklifts will be determined.

$$K_1=\sum T \ast U$$

(3)

$$K_2=\sum T \ast P$$

(4)

where

K₁—operating costs of the electric forklift [PLN].

K₂—operating costs of the combustion engine forklift [PLN].

U—cost of using 1 kWh.

P—cost of using 1 L of diesel.

The cost of 1 kWh and 1 L of diesel fuel was determined based on the current market level and amounted to U—1.10 PLN and P—5.90 PLN, respectively.

It should be emphasized that the cost analysis presented in this article covers only the costs related to electricity consumption and fuel combustion. Operating costs such as expenses for repairs, servicing, or infrastructure investments, including the construction and maintenance of vehicle charging stations, were excluded from the considerations.

The next stage, in line with the adopted research methodology, was the selection of an alternative solution to the currently used vehicles performing inter-operational transport in the analyzed case study. In this publication, the application of HFCV forklifts was considered (

Table 4. Parameters of HFCV forklifts.

Parameter	HFCV Forklift
Maximum load capacity	2 t
Driving speed	10 km/h
Fuel tank capacity	1.0 kg H2, 350 bar
Operation scope/time	8 h

).

HFCV forklifts are vehicles powered by fuel cells, in which hydrogen undergoes an electrochemical reaction with oxygen, producing electricity. This process does not generate exhaust gases, and the only by-product is water vapor. Thanks to this, HFCV forklifts combine the advantages of electric drive (quiet operation, the lack of local emissions) with the short refueling time characteristic of combustion vehicles.

The analysis covered the operational parameters generated by a fleet of thirteen such vehicles, corresponding to the number of forklifts currently used in the company, differing only in their power source. For the purposes of the analysis, it was assumed that so-called green hydrogen would be used in the case study, the production of which is not associated with CO₂ emissions. The unit cost of hydrogen (per 1 kg) was determined based on current prices in Poland, i.e., 69 PLN/kg.

$$K_3=\sum T \ast H$$

(5)

K₃—operating costs of an HFCV forklift [PLN]

U—cost of 1 kg of green hydrogen [PLN]

Based on the operational parameters of the HFCV forklift fleet, obtained through computer simulation conducted for the analyzed case study, results were developed to enable a comparative analysis between the current state and the variant assuming the replacement of existing vehicles with fuel-cell-powered forklifts. The objective function and the main evaluation criteria were defined as minimizing operating costs and CO₂ emissions, as well as increasing the utilization rate of individual units in the fleet.

$$\left\{\begin{array}{c}min Z_1=\sum _i^n{K}_i\\ min Z_2=\sum _i^n{E}_i\\ min Z_3=\sum _i^n{L}_i\end{array}\right.$$

(6)

where

Z₁—total operating cost of the forklift fleet.

Z₂—total CO₂ emissions generated by the forklifts.

Z₃—utilization level of the forklifts.

K_i—operating costs of the i-th type of forklift (K₁, K₂, K₃).

E_i—CO₂ emissions generated by the i-th type of forklift (E₁, E₂, E₃).

L_i—utilization index of the i-th forklift.

$$L={\displaystyle \frac{t_j}{T}}$$

(7)

t_j—actual operating time of the i-th forklift.

The objective function presented in Equation (6) was formulated to support a multi-criteria evaluation of the forklift fleet under different operational scenarios. Specifically, it focuses on three main goals: reducing total operating costs, lowering CO₂ emissions, and increasing the utilization rate of individual forklifts. In this study, the equation serves primarily as an analytical tool rather than a formal optimization algorithm, providing a clear framework to indicate areas for improvement. The results obtained for the hydrogen fuel cell variant were compared against the current state of the fleet, which consisted of electric and diesel-powered forklifts. In this context, the formulation illustrates the aim of the study: to achieve reductions in operating costs and emissions while improving fleet utilization, relative to the baseline scenario.

The utilization index of each forklift was calculated as the ratio of actual operating time to total available time, allowing for a precise assessment of how effectively individual units were employed during the simulation. This approach enabled a comprehensive comparison of the hydrogen fuel cell fleet’s operational, economic, and environmental performance with that of conventional forklifts in the analyzed case study.

In the conducted analysis, investment costs related to the implementation of the new solution were omitted, focusing exclusively on operating costs. At the same time, it was assumed that the use of hydrogen-powered forklifts aligns with current trends in the development of transport technologies, aimed at improving energy efficiency and reducing environmental impact.

The adopted research approach enables the analysis of both economic and ecological aspects, with particular emphasis on the potential for reducing carbon dioxide emissions and the impact of the implemented solution on key parameters of internal transport processes.

The evaluation of the effectiveness of the proposed technology was placed in the context of sustainable development, which makes it possible not only to assess its profitability and environmental impact, but also to identify the role that HFCVs may play in the transformation of industry towards more sustainable and low-emission logistics systems.

3. Results

3.1. Research Method

In line with the adopted research methodology, a simulation model was developed that realistically reflects the course of the actual logistics process, including the transport of semi-finished products and finished goods to designated storage areas (Figure 3).

The model was designed to reproduce all key parameters affecting the analyzed process. It takes into account, among others, the actual number of transported load units, the distances between individual infrastructure objects, the movement speeds of internal transport vehicles, as well as operations related to the loading and unloading of materials (

Table 5. Diagram of inter-operational transport (first variant).

Transportation from	Transport to	Number of Transports with and Without Cargo	Means of Transport
Queue 1	Queue 4	2800	Electric Transporter 1 Diesel transporter 1
Queue 2	Queue 4	1900	Electric Transporter 2 Diesel transporter 2
Queue 3	Queue 6	3100	Electric transporter 3 Electric transporter 4 Diesel transporter 3 Diesel transporter 4
Queue 5	Queue 6	2200	Electric transporter 5 Diesel transporter 5
Queue 7	Queue 8 and 9	3900	Electric transporter 6 Electric transporter 7 Diesel transporter 6

).

In the next stage of the study, a simulation was conducted for a logistics variant involving a fleet of thirteen hydrogen-powered HFCV forklifts. These vehicles performed an analogous process of transporting goods between the various locations of the logistics system, in accordance with the assumptions adopted in the base model (

Table 6. Diagram of the inter-operational transport carried out by HFCV forklifts (second variant).

Transportation from	Transport to	Number of Transports with and Without Cargo	Means of Transport
Queue 1	Queue 4	2800	HFCV Transporter 1 HFCV Transporter 2
Queue 2	Queue 4	1900	HFCV Transporter 3 HFCV Transporter 4
Queue 3	Queue 6	3100	HFCV Transporter 5 HFCV Transporter 6 HFCV Transporter 7 HFCV Transporter 8
Queue 5	Queue 6	2200	HFCV Transporter 9 HFCV Transporter 10
Queue 7	Queue 8 and 9	3900	HFCV Transporter 11 HFCV Transporter 12 HFCV Transporter 13

).

Including this variant made it possible to compare the operational and cost efficiency of hydrogen-powered forklifts with electric and combustion vehicles, and thus to assess the potential benefits of applying hydrogen technologies in internal transport.

As already mentioned, the simulation was conducted using the FlexSim software, which was employed to replicate the case study under analysis based on the implemented data. The simulation allowed for detailed modeling of forklift operations, including process execution times, fleet utilization, and operational flow, providing a realistic representation of the internal transport system. This approach enabled a comprehensive assessment of the performance, economic implications, and environmental impact of hydrogen fuel cell forklifts under conditions closely resembling actual production operations. All data used in the simulation were sourced from the Materials and Methods section.

3.2. Results of Computer Simulations

3.2.1. Simulation of the First Variant (Using Electric Transporter and Diesel Transporter)

As a result of the conducted simulation, the obtained results indicated that the total time taken to complete the analyzed logistics process amounted to 31.35 h.

The analysis of the load on internal transport vehicles showed a varied level of their utilization, which was primarily due to differences in route lengths and the frequency of transport operations (Figure 4).

The highest utilization level was recorded for Diesel Transporter 1, which performed transport between Queue 1 and Queue 4. This forklift exhibited a high work intensity due to the necessity of servicing a section of strategic importance within the overall material flow process.

Conversely, the lowest utilization level was observed for Electric Transporter 6, operating on routes between Queue 7 and Queue 8 and Queue 9. For this vehicle, the actual operating time, including both product transport and return trips to pick up the next load, accounted for only about 25% of the total logistics process time. The low utilization rate was a consequence of short transport distances and limited demand for service in this area.

The average utilization level of all forklifts involved in the analyzed logistics process was 51.40% (Figure 5).

It should be emphasized, however, that the overall utilization level of electric forklifts was more than 6 percentage points lower compared to combustion-powered forklifts (Figure 6).

The result, equal to 46.21%, was influenced by the need for periodic battery charging. The charging process introduced additional work interruptions, which directly contributed to increased idle time and reduced operational efficiency of the electric forklifts.

As a result of the simulation, the obtained data formed the basis for determining the CO₂ emission level generated by electric forklifts (E1) (Figure 7).

Based on the calculations presented in the research methodology in Section 2, it was found that the total CO₂ emission generated by electric forklifts amounted to 325.10 kg.

For combustion-powered forklifts, the total CO₂ emission was 328.45 kg (Figure 8).

In the next stage of the study, the costs generated by all internal transport vehicles involved in the movement of goods within the analyzed case study were determined (Figure 9).

The total cost of completing the entire logistics process amounted to 2425.20 PLN. A comparative analysis revealed significant differences between the operating costs of electric forklifts and diesel-powered vehicles. The total operating cost of electric forklifts was 1190.20 PLN lower compared to the costs of using diesel-powered forklifts.

3.2.2. Simulation of the Second Variant (Using HFCV Transporter)

The total duration of the process in the analyzed variant amounted to 28.40 h. The reduction of almost three hours in transport completion time resulted primarily from the absence of the need to recharge HFCV forklifts, which had been a significant factor extending the process duration in the case of electric forklifts.

The utilization level of internal transport vehicles also showed variation, similar to the model including both electric and combustion forklifts (Figure 10).

The average utilization level of all forklifts involved in the analyzed production process was 59.47%, which is more than 8 percentage points higher compared to the variant including electric and combustion forklifts (Figure 11).

During the development of the case study methodology, it was assumed that green hydrogen would be used for refueling HFCV forklifts. Therefore, CO₂ emission factors were not considered in this variant. Instead, the focus was on determining the costs associated with performing transport operations (Figure 12).

Based on the obtained results, it was found that the total operating cost of the HFCV forklift fleet amounted to 1894.0 PLN. This means that the overall cost of using this type of internal transport vehicle was more than 530.0 PLN lower, corresponding to a reduction of approximately 78 percentage points compared to the costs of the variant including electric and combustion forklifts.

Table 7. Comparative analysis of obtained results.

Parameter	Variant 1		Variant 2
	Electric-Powered Forklifts	Combustion Engine Forklifts	HFCV Forklifts
	Electric-Powered and Combustion Engine Forklifts (Together)
Process duration [h]	-	-	28.40
	31.35
CO2 emission [kg]	325.10	328.45	-
	653.55
Cost [PLN]	617.5	1807.70	1894.0
	2425.2
Utilization percentage [%]	46.21	55.86	59.47
	51.40

presents a comparative summary of the two verified variants of transport process organization, constituting a comprehensive comparative analysis. The first variant (Variant 1) included a fleet of electric and combustion-powered forklifts, while the second variant considered only HFCV forklifts.

Including the same case study—with an identical number of transports, the same fleet structure and size, and the same routes between locations—allowed for a direct comparison of results between the two variants. This approach provided an objective assessment of key efficiency and cost parameters, and the obtained results served as a reliable reference point for comparative analysis.

4. Discussion

The analysis conducted in this study allows for a preliminary assessment of the effectiveness of implementing fuel cell forklifts in a production environment. The results indicate the potential of HFCV technology to reduce internal transport times and increase fleet utilization compared to traditional electric and combustion forklifts. In line with previously identified research gaps, this case study provides data combining economic, ecological, and operational aspects within the context of computer simulations of real forklift fleet scenarios, which has not been widely analyzed in the literature.

It should be emphasized, however, that the study is based on a single case study in a production facility specializing in steel products. Therefore, the results are illustrative and may only indicate general trends regarding the application of HFCV forklifts compared to the more common electric and combustion-powered options. The conclusions cannot be directly extrapolated to all types of production facilities, but they provide a valuable basis for further research on the potential of hydrogen fuel cells in internal transport.

Further research should focus on a comprehensive assessment of HFCV operating costs across the full product life cycle, considering both investment costs for hydrogen refueling stations and operational costs. Such an analysis would allow a more accurate determination of the economic viability of deploying hydrogen forklifts in different production and logistics scenarios, as well as a comparison of their economic and environmental efficiency with more traditional solutions. Additionally, the analysis could be made more realistic by incorporating a range of energy cost scenarios in the comparative assessment, which would help to better understand how fluctuations in energy prices impact the overall economic performance of HFCV fleets.

5. Conclusions

In this study, a simulation model of the internal transport process in a production facility was developed using FlexSim software. The model replicated key parameters of the logistics system, including the number of transported units, distances between locations, forklift speeds, and loading/unloading operations. FlexSim was chosen for its functionality in modeling dynamic material flow systems, its visualization capabilities, and its widespread use in the literature.

Simulation of the electric and combustion forklift fleet showed that the total process duration was 31.35 h, with an average forklift utilization of 51.40%. Electric forklifts exhibited lower utilization (46.21%) due to interruptions for battery charging. The total CO₂ emissions for the electric fleet amounted to 325.10 kg, while for the combustion fleet it was 328.45 kg. The operating costs of electric forklifts were 1190.20 PLN lower than those of the diesel variant, with a total process cost of 2425.20 PLN.

In the HFCV fleet scenario, the total process duration decreased to 28.40 h, and average fleet utilization increased to 59.47%. The use of green hydrogen allowed CO₂ emissions to be excluded from the analysis. HFCV fleet operating costs amounted to 1894.0 PLN, representing savings of approximately 530.0 PLN compared to the electric and combustion variants. The reduction in process duration and the increased utilization of HFCV forklifts were primarily due to the absence of refueling or charging interruptions, which significantly improved operational continuity.

This analysis confirms that HFCV forklifts can enhance internal transport efficiency and reduce operating costs under real production conditions. It should be noted, however, that the study considered only energy- and fuel-related operating costs, excluding investment costs such as hydrogen refueling infrastructure, which could significantly affect the total cost of implementing HFCV technology.

It is also worth noting the limitations of the adopted methodology. The case study did not include investment costs associated with implementing hydrogen refueling infrastructure, which represents a significant component of the total operating costs of an HFCV fleet. The absence of this analysis may underestimate the actual financial outlays required for full technology deployment, particularly when compared to electric forklifts, which require only battery charging infrastructure.

Another important generalization applied in this case study was the assumption of a constant energy price, whereas in reality energy costs may vary significantly depending on their source, and their increase or decrease could substantially affect the conclusions drawn. Moreover, the analysis considered only one type of hydrogen production pathway (the most environmentally friendly option) while the use of alternative hydrogen generation methods could lead to different outcomes.

The computer simulation methodology allowed a comprehensive evaluation of the efficiency of three drive types in terms of process duration, forklift utilization, and operating costs. The results provide a basis for further research, including a full life-cycle cost analysis of HFCV forklifts, infrastructure expenditures, and comparisons with electric and combustion solutions across different production environments.