## Abstract

In view of serious environmental problems occurring around the world and in particular climate change caused significantly by dangerous CO2 emissions into the biosphere in the developmental process, it has become imperative to identify alternative and cleaner sources of energy. Compressed hydrogen is being considered as a potential fuel for heavy-duty applications because it will substantially reduce toxic greenhouse gas emissions and other pollutant emissions. The cost of hydrogen will be the main element in the acceptance of compressed hydrogen internal combustion engine (ICE) vehicles in the marketplace because of its effect on the levelized cost of driving. This paper investigates the feasibility of developing a nationwide network of hydrogen refueling infrastructure with the aim to assist in a conversion of long-haul, heavy-duty (LHHD) truck fleet from diesel fuel to hydrogen. This initiative is taken in order to reduce vehicle emissions and support commitments to the climate plans reinforcing active transportation infrastructure together with new transit infrastructure and zero-emission vehicles. Two methods based on constant and variable traffics, using data about hydrogen infrastructure and ICE vehicles, were created to estimate fueling conditions for LHHD truck fleet. Furthermore, a thorough economic study was carried out on several test cases to evaluate how diverse variables affect the final selling price of hydrogen. This gave an understanding of what elements go into the pricing of hydrogen and if it can compete with diesel in the trucking market. Results revealed that the cost to purchase green hydrogen is the utmost part in the pump price of hydrogen. Due to the variety in hydrogen production, there is no defined cost, which renders estimates difficult. Moreover, it was found that the pump price of green hydrogen is on average 239% more expensive than diesel fuel. The methodology proposed and models created in this feasibility study may serve as a valuable tool for future techno-economics of hydrogen refueling stations for other types of ICE fleets or fuel cell vehicles.

## 1 Introduction

Climate change stays a serious problem touching every facet of the natural environment. It is recognized that climate change is the direct consequence of natural sources, but mainly relatable to anthropogenic actions, and several scientific studies definitely ascertain that global warming is accountable to harshly changing the balance in the Earth’s climate via emissions of harmful greenhouse gases (GHGs).

On December 12, 2015, about 195 countries signed the Paris Agreement. The main objective is to limit the global temperature increase to 1.5 °C from pre-industrial levels [1]. Since then, many states have been performing research to identify which sectors can be improved to help attain this target. Vehicles are one main part of the concern, both domestically and internationally. Road freight transport represents a substantial part of total energy utilization in the transport sector. Nearly 45% of total transport energy use links to freight transport, with heavy-duty vehicles (HDVs) consuming more than half of that energy [1]. Additionally, road freight transport relies much on fossil fuels; with medium and heavy freight trucks representing 24% of total oil-based fuel utilization. Diesel is the principal fuel employed in road freight transport, accounting for 84% of all oil commodities consumed and in proportion to half of the total diesel demand. Despite the small share in road vehicles, medium-duty vehicles (MDVs) and HDVs contribute exceedingly to transport GHG and air pollutant emissions, and fossil fuel consumption. This is caused by high truck fuel consumption, significant annual traveled ranges, and prolonged idling times. In the European Union, HDVs represent 30% of on-road GHG emissions, despite accounting for merely 4% of the road vehicle fleet. Also, in the United States, MDVs and HDVs constitute 26% of transport GHG emissions. Additionally, road freight trucks generate half of the particulate matter (PM) emissions and one-third of NOx emissions of the transport sector in cities. In addition, diesel exhaust emission is classified as carcinogenic to humans (Group 1) by the World Health Organization (WHO) [2,3]. Furthermore, in Canada, in 2018, the transport sector was the second-largest source of GHG emissions, accounting for 25% (185 mega tones of carbon dioxide equivalent) of total national emissions. Between 1990 and 2018, GHG emissions from the transportation sector grew by 53%. The growth in emissions was mostly driven by increases from freight trucks and passenger light trucks [4].

All the time, most of merchandises carried by road in North America are transferred by heavy-duty truck. In effect, trucks transport about 90% of totally customer goods and foods transacted from Canada to the United States. Though stock travel by truck is obviously a pillar of the economy, it is likewise a main cause of GHGs: more than 10.5% of GHG emissions emanate from cargo transport, besides most of these emissions derive from heavy-duty trucks. With truck activities intensifying and less vehicle efficiency improvements compared to light vehicles, emissions from cargo are projected to circumvent those from commuter transport through near 2030. Reinforcing a change to a purer trucking segment needs to be a leader of climate action plans across the planet if total emissions are to be reduced by around 45% from 2010 levels on or after 2030 [5,6].

The topic of application for this paper is on long-haul, heavy-duty (LHHD) trucks, particularly converting the main source of fuel from diesel to hydrogen. Hydrogen was chosen as it can be used as an energy carrier in modified internal combustion engines (ICEs), which helps eliminate the need to purchase new vehicles. The existing diesel engines can be easily modified to support hydrogen fuel, making the adoption of hydrogen both easier and more cost-effective than implementing fuel cells [710].

One obstacle, with converting LHHD truck fleet to hydrogen, is the lack of current refueling infrastructure. Irrespective of the percentage of the truck fleet that is adapted to hydrogen, it is required to have a refueling network that is safe, accessible, and economically feasible. The final selling price of hydrogen is a sound criterion to the feasibility of investing in such an infrastructure. If this selling price is proved to be viable compared to that of diesel, then it can be stated that it is economically justified to execute such a project. Elements that go into the final selling price of hydrogen comprise the project capital costs, operational costs, and the amount of fuel required to support the LHHD truck fleet.

There have been in the literature some technical and economic studies on natural gas (NG) and alternative fuels such as renewable natural gas (RNG) and also on NG/RNG refueling infrastructure [1118]. While there have been similarly numerous works on the technical, economic and ecological aspects related to hydrogen vehicles and refueling infrastructure [1932], there is a lack of feasibility studies on hydrogen refueling infrastructure for ICE-powered LHHD trucks. Hence, this paper seeks to assess the feasibility of realizing a nationwide network of hydrogen refueling stations with the purpose to assist in the conversion of the LHHD truck fleet from diesel fuel to hydrogen. This initiative is taken in order to reduce vehicle emissions and support commitments to the climate plans supporting active transportation infrastructure, together with new transit infrastructure, and zero-emission vehicles. Two methods based on constant traffic and variable traffic, with data on hydrogen infrastructure and vehicles, are created to estimate fueling conditions for the LHHD truck fleet. In addition, a detailed economic analysis was performed on several test cases to evaluate how diverse variables affect the final selling price of hydrogen. This will provide insight with the understanding of what factors go into pricing hydrogen and if it can compete against diesel in the trucking market.

The structure of this paper is as follows: in Sec. 2, some background information on hydrogen ICE-based vehicles and refueling stations is provided. Sections 3 and 4 discuss in detail the technical and economic analyses, as well as the results and their discussion, while the conclusions are presented in Sec. 5.

## 2 Overview on Compressed Hydrogen Gas Vehicles and Refueling Stations

This section presents background overviews and input data for the case study on LHHD transport trucks, hydrogen ICE vehicle, hydrogen gas production, and hydrogen refueling infrastructure.

### 2.1 Long-Haul, Heavy-Duty Transport Trucks.

For the input data needed for the evaluation of the hydrogen infrastructure, a case study was considered for the LHHD ICE truck fleet. As the focus of this study, LHHD trucks can be defined as those that weigh more than 14,971 kg [33] and travel more than 320 km to make a delivery [34]. It is estimated that a fleet of 70,000 LHHD trucks is in operation [18]. According to a 2018 analysis of trucking in the United States, nearly 90% of respondents used diesel, or a bio-diesel blend, as their source of fuel [35]. From this figure, it will be assumed that all LHHD trucks use diesel. A database on road transportation showed that the average fuel efficiency for heavy-duty trucks is 0.309 L/km [36].

Any given heavy-duty truck has the capacity to hold 470–1100 L of fuel, split between two evenly loaded tanks on either side of the truck cabin [37]. Due to the nature of long-haul driving, it is assumed that trucks will have the capacity to hold, the higher value, 1100 L of fuel. This would better accommodate the long distances traveled and would give more security to truck drivers if they are distant from a refueling station.

Truck drivers either operate on a 7-day or 14-day schedule, which for this analysis a 7-day schedule will be assumed. The Ministry of Transportation requires that an operator must take 10 h rest after a maximum 14-hour shift, where a maximum of 13 h can be spent actively driving [38]. While an operator can theoretically drive for 13 h, packing, unpacking, refueling, and checks all take time and will reduce the time spent driving. Using the 7-day schedule and the 2 weeks of vacation entitled to each employee [39], an estimate can be made as to how many days per year an operator can be driving, which comes to 298 days per year. Another report analyzing LHHD trucks estimates that on average, an operator will drive 825 km per day [18].

An interesting factor that must be taken into account, specifically with long-haul vehicles, is the time spent per day idling. Idling is defined as having the engine run slowly while disconnected from a load [40]. According to a report by the U.S. Department of Energy, idling a heavy-duty truck will consume about 3 L of diesel per hour [41]. If it is assumed that a truck will idle for 11 h per day, then this idling consumes 11.5% of the total fuel used per day. This is significant enough to be taken into consideration during further analysis.

### 2.2 Overview on Hydrogen Vehicles.

Hydrogen vehicles are defined as any vehicle that uses hydrogen (in either liquid or gaseous state) as its source of fuel. The main components include the internal combustion engine, the fuel tank, and the various control systems. Hydrogen internal combustion engines use a spark-ignition system, in contrast to compression-ignited engines seen with diesel-powered trucks. The spark plugs should be cold-rated, meaning that between sparks the temperature is as low as possible, in order to avoid pre-ignition [7,8]. Due to hydrogen’s very low pressure, fuel tanks may be pressurized up to 70 MPa [42] to hold more fuel. The main alternatives for onboard hydrogen storage are gaseous storage at 350 or 700 bar hydrogen storage in thermally insulated tanks or in materials with superior chemical properties, like metal hydrides or organic compounds. Currently, the leading research emphasis is on 350 bar and 700 bar storages in Type III (metallic liner) or Type IV (plastic liner) tanks, the former having enhanced gravimetric storage effectiveness [42]. Type IV pressure tanks hold a plastic liner overwrapped by costly carbon-fiber composite material to give strength. The application of carbon-fiber composites contributes to considerably lower weight than all-metal pressure tanks may have. The utilization of Type IV pressure tanks, though, rises the cost of storing hydrogen in LHHD vehicles, principally because of the high cost of the carbon-fiber composite material. According to the analysis provided in Ref. [43], it is estimated that a 700 bar (10,152 psi) Type IV hydrogen storage tank for a heavy-duty truck would cost $15,000–17,500, which would represent about 1.50–1.75 times the cost of a 248 bar (3600 psi) Type IV CNG (compressed natural gas) storage tank ($10,000).

Due to a lack of data on the fuel economy for hydrogen LHHD trucks, an estimate was done using other available data. The difference in fuel economics for diesel pickup trucks (0.103 L/km [44] and diesel LHHD trucks (0.309 L/km) was found to be 200%. This was applied to the fuel economy of a hydrogen pickup truck, which was found to be, on average, 0.117 diesel liters equivalent (DLE)/km [45,46], resulting in a hydrogen LHHD truck fuel economy of 0.351 DLE/km, where DLE is the diesel equivalent, in liters, of hydrogen used.

A metric that helps with the comparison between hydrogen and diesel vehicles is the storage volume ratio. This is the ratio of the hydrogen to diesel volumes needed to store an equivalent amount of energy and was found using the energy-mass equivalence between hydrogen and gasoline (1 kg hydrogen = 2.8 kg gasoline [47]), the density of compressed hydrogen (41 kg/m3 [47]), the density of diesel (0.85 kg/L [18]), and the specific energy densities of gasoline and diesel (46.4 MJ/kg and 45.6 MJ/kg, respectively [11,48]). The resulting ratio is 7.28 L hydrogen/L diesel.

### 2.3 Overview on Hydrogen Gas Production.

Hydrogen is the most abundant element and lightest element in the universe. On Earth, however, it is rarely found isolated and requires some variety of processes to extract. Common extraction processes include natural gas reforming, electrolysis, biomass gasification, and coal gasification. Differences between these may include the cost to operate, process efficiency, and total greenhouse emissions produced. Figure 1 adapted from Ref. [46] shows the total greenhouse emissions for different hydrogen production pathways, compared to those of conventional vehicle fuels. Note that there is no “pump-to-wheels” emissions for the hydrogen pathways as it does not produce greenhouse gas emissions like those found with conventional fuels when combusted in an internal combustion engine.

Fig. 1
Fig. 1
Close modal

Each hydrogen production pathway involves separating hydrogen from some compound. Natural gas (hydrocarbon gas mixture consisting primarily of methane, but usually containing varying amounts of other higher alkanes, and sometimes a minor percentage of carbon dioxide, nitrogen, hydrogen sulfide, or helium), can be combined with high-temperature, high-pressure steam, in the presence of a catalyst, to form hydrogen and carbon monoxide. While the hydrogen is separated, the carbon monoxide is further reacted with steam and a catalyst, called the water–gas shift reaction, to produce more hydrogen and carbon dioxide. This described process is called natural gas reforming [1921,49]. The resulting carbon dioxide can be captured or released into the atmosphere.

Water (H2O) can also be used to produce hydrogen in a reaction called electrolysis. The first step in the process occurs at the anode, in which water reacts to produce oxygen gas and positively charged hydrogen ions. The hydrogen ions then travel through a membrane where they will react at the cathode to produce hydrogen gas. An electrolyzer, a system that produces hydrogen via electrolysis, can have a liquid electrolyte (typically an alkaline solution of sodium or potassium hydroxide), or a solid electrolyte (polymer electrolyte membrane (PEM) or ceramic) [1921,50,51].

An organic compound, in this situation biomass or coal, can be reacted with high-temperature oxygen gas and steam. This will form hydrogen, carbon dioxide, and carbon monoxide. The hydrogen is captured, the carbon dioxide is captured or released, while the water–gas shift reaction converts the carbon monoxide to more hydrogen and carbon dioxide. This process is called gasification [1921,52,53].

Regardless of the hydrogen production pathway, the process can occur at a central facility, or locally where it is needed. A central scheme can increase the scope of production by producing hydrogen at large facilities that take advantage of bulk processes to reduce costs per unit. However, hydrogen needs to be transported to its destination via pipeline or trucks. This will increase the operational fees due to the need to install a dedicated hydrogen pipeline or operate a fleet of hydrogen tanker trucks. A distributed process, one where hydrogen is produced on demand where it is needed, eliminates the need to deliver hydrogen over long distances. This, however, increases operational costs due to more equipment and lower production output. Hydrogen costs vary with the production pathway. Since this study is analyzing the production of green hydrogen using renewable sources, only sources that use renewable methane or biomass will be considered. As a low estimate for the cost of green hydrogen, a price of $226.32/m3 was used [54]. As a high estimate for the cost of green hydrogen, a price of$298.08/m3 was used [51]. As an average estimate for the cost of green hydrogen, a price of $261.93/m3 was used [47,51,54,55]. ### 2.4 Hydrogen Refueling Infrastructure. Hydrogen stations are built to refuel vehicles at similar rates to those used by diesel stations. Hydrogen is typically dispensed at 34.5 MPa [47]; however, large vehicle storage tanks can accommodate pressures up to 70 MPa [3032]. The refueling station footprint will depend on whether hydrogen is being produced onsite or being delivered. However, each refueling station will consist of a compressor, storage tanks, and at least one dispenser, as well as a small building to store maintenance items and a workspace for employees. Figure 2 adapted from [56] shows the basic components and configurations of gaseous refueling stations. Fig. 2 Fig. 2 Close modal Without a reliable network of refueling infrastructure, managing a vehicle fleet is nearly impossible. Many factors go into the design of a national refueling system, many of which depend heavily on the type of vehicles being used and the surrounding road infrastructure. Canada is a unique country in part because 66% of the population live within 100 km of the U.S. border [57]. This allows for a highway system that is relatively simple when compared to other countries, such as the United States. In order to simplify the analysis, it was assumed that LHHD trucks only traveled along the Trans-Canada Highway and Highway 401. The former moves traffic along the southern border from British Columbia to Newfoundland and Labrador, while the 401 facilitates heavy traffic from North-Eastern United States, through Southern Ontario, and into Quebec. Hydrogen refueling stations will have different cost factors, when compared to diesel refueling stations. Most refueling stations that have been implemented have a relatively low capacity. This is because of the small market that uses the infrastructure. For this study, as can be seen in Sec. 3, refueling stations will need to be significantly larger than what currently exists. This results in a lack of information on the capital costs required for large-capacity hydrogen refueling stations. To compensate for this, a cost model was developed, so that capital costs can be estimated for this work. ## 3 Technical Analysis Technical analysis was carried out to estimate the number of hydrogen refueling stations that would be needed to assist in converting the fleet of LHHD vehicles to hydrogen. Two methods based on constant and variable traffics were created with the aim to compare results for diverse conditions. For each method, five cases were built to analyze the results at varying fleet penetration levels of 10%, 25%, 50%, 75%, and 100%. Fleet penetration is expressed as the percentage of LHHD truck fleet that becomes converted to operate on hydrogen. For this study, refueling stations will need to be significantly larger than what currently exists. A cost model was created in order to determine the capital costs. A linear relationship was assumed between the storage capacity of a refueling station and its capital cost. Data for the model were taken from a report by the National Renewable Energy Laboratory [29] and consisted of CAD$6.97 million for a 1500-kg capacity station and CAD$4.26 million for a 600-kg capacity station. From these two points, a linear equation was created, as shown by Eq. (1) $y=2.453333+0.003011111x$ (1) Table 1 summarizes the data used in the analysis. Table 1 Input data summary DescriptionValueUnit Long-haul, heavy-duty truck fleet size70,000trucks Diesel engine average fuel efficiency0.309L/km Hydrogen engine average fuel efficiency0.351L/km Diesel fuel capacity1100L Average distance traveled per day per truck825km % of fuel tank that gets filled at refueling stations75% Time one truck spends at fuel pump0.25h Operating hours of refueling stations/day24h Days spent driving per year298days/year Days spent before refueling2days Fuel consumed per day while idling33L DescriptionValueUnit Long-haul, heavy-duty truck fleet size70,000trucks Diesel engine average fuel efficiency0.309L/km Hydrogen engine average fuel efficiency0.351L/km Diesel fuel capacity1100L Average distance traveled per day per truck825km % of fuel tank that gets filled at refueling stations75% Time one truck spends at fuel pump0.25h Operating hours of refueling stations/day24h Days spent driving per year298days/year Days spent before refueling2days Fuel consumed per day while idling33L ### 3.1 Constant Traffic Method. This method examines to determine the storage capacity of three pre-determined cases. The first case assumes 87 refueling stations exist, this is one station every 100 km. The second case assumes 44 refueling stations exist, this is one every 200 km. The third case assumes 29 refueling stations exist, this is one every 300 km. This is all done under the assumption that the traffic distribution of LHHD trucks does not change with geography. The basis for this assumption is the nature of long-haul trucks as they drive long distances to deliver cargo, instead of traveling within a region delivering cargo. To obtain the number of storage capacities, first, it was needed to find the total consumption of fuel throughout the truck fleet. This was done, as presented in Eq. (2), by summing the fuel consumed per day while driving and idling, then multiplying that value by the fleet size $Vfc=nt×rV1000×(ηf×dd+ti×V˙i)$ (2) With the total fuel consumption per day for the truck fleet, the storage capacities can be found by dividing the number of refueling stations by the fuel consumption per day, as can be seen in Eq. (3) $Vrs=Vfcnrs$ (3) The results of the analysis are shown in Table 2. As can be seen, the stations required for a refueling network, even at the lowest penetration level of 10%, are much greater than what was defined by the National Renewable Energy Laboratory’s report as a large station capacity of 1500 kg [29]. Table 2 Results from technical analysis using the constant traffic method % of Truck fleet convertedNumber of trucksTotal H2 required per day (m3)Storage capacity per station (m3)Storage capacity per station (kg) Number of refueling stations8610700016,455.2190.37800.5 2517,50041,138.1475.619,501.2 5035,00082,276.1951.339,002.5 7552,500123,414.31426.958,503.7 10070,000164,552.41902.678,005.0 4310700016,455.2380.515,601.0 2517,50041,138.1951.339,002.5 5035,50082,276.11902.678,005.0 7552,500123,414.32853.8117,007.4 10070,000164,552.43805.1156,009.9 2910700016,455.2570.823,401.5 2517,50041,138.11426.958,503.7 5035,00082,276.12853.8117,007.4 7552,500123,414.34280.8175,511.1 10070,000164,552.45707.7234,014.8 % of Truck fleet convertedNumber of trucksTotal H2 required per day (m3)Storage capacity per station (m3)Storage capacity per station (kg) Number of refueling stations8610700016,455.2190.37800.5 2517,50041,138.1475.619,501.2 5035,00082,276.1951.339,002.5 7552,500123,414.31426.958,503.7 10070,000164,552.41902.678,005.0 4310700016,455.2380.515,601.0 2517,50041,138.1951.339,002.5 5035,50082,276.11902.678,005.0 7552,500123,414.32853.8117,007.4 10070,000164,552.43805.1156,009.9 2910700016,455.2570.823,401.5 2517,50041,138.11426.958,503.7 5035,00082,276.12853.8117,007.4 7552,500123,414.34280.8175,511.1 10070,000164,552.45707.7234,014.8 ### 3.2 Variable Traffic Method. This method examines to find the number of hydrogen refueling stations required to suit the LHHD truck fleet by evaluating the traffic distribution with respect to geography, along major highways. As mentioned in the previous section, the Trans-Canada highway and highway 401 were presumed to be the only highways used by LHHD trucks, which totals 8649 km [58,59]. The Ontario Ministry of Transportation (MTO) established a database, which indicates the annual average daily traffic (AADT) for each stretch of known highway in Ontario [60]. The highway 401 was considered in this study. It is broken down into sub-routes with their distance and annual average daily long-haul truck traffic (AADLHTT) used to estimate the average distance between existing truck stops needed for developing a network of refueling stations on that route. Sections were visually created to help with the analysis, as shown in Fig. 3. Fig. 3 Fig. 3 Close modal To process the data, the average distance between existing truck stops was needed. This differs from the first analysis as arbitrary distances were assumed previously. Rather, it defines the number of refueling stations needed and then estimates the capacity necessitated to assist the fleet. The mean distance was acquired using a database of Esso truck stops and totaled to 110 km [61]. A hypothesis was made that Esso would set up a truck stop network so that, if required, trucks would simply have to depend on Esso for fuel, hence rising revenue for the company. By means of these data, the number of refueling stations per section was found by dividing the length of the section by the average truck stop distance and then rounding up to the nearest whole number, shown by Eq. (4) $nrs=⌈dsdavg⌉$ (4) The total volume of hydrogen required per day is estimated using a derived equation (Eq. (5)) below $Vfc=ds×ηf×rV×n˙t1000$ (5) There was no consolidated database, similar to the one MTO provided for highway 401 that existed for the Trans-Canada Highway. Because of that, paths from Fig. 3 were utilized to the Trans-Canada Highway. It was supposed that within 100 km on either side of a major city resting along the Trans-Canada Highway, traffic would be similar to that found in Fig. 3. It was then assumed that the rest of the highway would have similar traffic to that found in Secs. 1 and 3 from Fig. 3, Eqs. (4) and (5) were used using these hypotheses. Table 3 provides a summary of the results from the technical analysis using the Variable Traffic method. The results indicate that there will be an initial phase when all 81 refueling stations are constructed to accommodate 86 kg of hydrogen each. Afterward, as more trucks are converted to hydrogen, upgrades will take place to expand the capacity of each refueling station. Table 3 Results from technical analysis using the variable traffic method % of truck fleet convertedNumber of trucksNumber of stations requiredStorage capacity per station [kg] 1070008186 2517,50081216 5035,00081432 7552,50081648 10070,00081864 % of truck fleet convertedNumber of trucksNumber of stations requiredStorage capacity per station [kg] 1070008186 2517,50081216 5035,00081432 7552,50081648 10070,00081864 ## 4 Economic Analysis The aim of the economic analysis is to evaluate the final selling price of hydrogen to guarantee that the investment made to install the infrastructure is recovered. To define this, the annual income needed to fulfill this objective was found by means of a variation of the net present value (NPV) equation, per Eq. (6) [62] $I=−(B+C∑n=1nmax(1+i)−n)∑n=1nmax(1+i)−n$ (6) Table 4 presents the inputs to Eq. (6) as either a value or an equation. Table 4 Input data for the economic analysis VariableDescriptionValue/equation BTotal investment required ($)Number of stations × capital per station
CAnnual costs ($/year)(fuel consumed per year × fuel purchase rate)/(1—maintenance cost %) $i$Rate of return6% nmaxInfrastructure lifecycle (years)20 Cost of maintenance (%)27.5 [63] VariableDescriptionValue/equation BTotal investment required ($)Number of stations × capital per station
CAnnual costs ($/year)(fuel consumed per year × fuel purchase rate)/(1—maintenance cost %) $i$Rate of return6% nmaxInfrastructure lifecycle (years)20 Cost of maintenance (%)27.5 [63] Using Eq. (6) to find the annual income required for the project, the final selling price of hydrogen could be estimated by dividing the annual income by the total volume of fuel consumed per year, as provided in Eq. (7) $F=Iηf×dd×nt×nd$ (7) ### 4.1 Initial Analysis and Results. A baseline case, using technical data taken from the constant traffic method, was evaluated using Eqs. (6) and (7). Table 5 presents the input data and results (pump price [$/DLE]). It can be seen that the final selling price of hydrogen is not expected to vary significantly with variations to fleet penetration, hydrogen refueling station storage capacity, or the capital cost per hydrogen refueling station. A further variable that may vary considerably is the cost to purchase hydrogen from suppliers. After this preliminary analysis, it was presumed that this is the utmost influencing element to the final selling price of hydrogen.

Table 5

Results from an initial economic analysis using data taken from the Constant Traffic method

% of Truck fleet convertedNumber of trucksCapacity per station (kg)Cost per station (MCAD$)Pump price (CAD$/DLE)
Number of refueling stations86107000780025.92.95
2517,50019,50161.22.93
5035,00039,002119.92.92
7552,50058,504178.62.92
10070,00078,005237.32.92
4310700015,60149.42.93
2517,50039,002119.92.92
5035,00078,005237.32.92
7552,500117,007354.82.92
10070,000156,010482.22.92
2910700023,40172.92.93
2517,50058,504178.62.92
5035,000117,007354.82.92
7552,500175,511530.92.92
10070,000234,015707.12.92
% of Truck fleet convertedNumber of trucksCapacity per station (kg)Cost per station (MCAD$)Pump price (CAD$/DLE)
Number of refueling stations86107000780025.92.95
2517,50019,50161.22.93
5035,00039,002119.92.92
7552,50058,504178.62.92
10070,00078,005237.32.92
4310700015,60149.42.93
2517,50039,002119.92.92
5035,00078,005237.32.92
7552,500117,007354.82.92
10070,000156,010482.22.92
2910700023,40172.92.93
2517,50058,504178.62.92
5035,000117,007354.82.92
7552,500175,511530.92.92
10070,000234,015707.12.92

The analysis performed in the previous section was applied to six diverse cases. This was to understand the influences that the purchase price of hydrogen had on its final selling price. Data from both the Constant Traffic Method and the Variable Traffic Method were utilized for that analysis. Since Table 5 revealed that fleet penetration, storage capacity, and capital cost do not substantially alter the price of fuel, a single scenario was taken from each method to be utilized in this evaluation. Cases 1, 2, and 3 used a scenario of 100% fleet penetration and 44 refueling stations, from the Constant Traffic Method. Case 1 assumed a purchase price of hydrogen at the lower estimate of CAD$226.32/m3. Case 2 assumed an average purchase price of hydrogen at CAD$261.93/m3. Case 3 assumed a purchase price of hydrogen at the higher estimate of CAD$298.08/m3. Cases 4, 5, and 6 used a scenario of 100% fleet penetration and 81 refueling stations, from the Variable Traffic Method. Similar alterations in the purchase price of hydrogen were used in Cases 4–6, as were used in Cases 1–3. A summary of the input data is given in Table 6. The results are reported in Table 7 and Fig. 4. The selling price of hydrogen for the six different cases is compared to that of tax-free and taxed diesel and natural gas [64], with everything normalized to the cost per DLE. The results show that the price of hydrogen can vary from CAD$2.3/DLE to CAD$3.3/DLE and rises linearly by CAD$.05/DLE for every CAD$1/GJ rise in the cost of hydrogen. These results reveal that the price of hydrogen is expected to remain higher than that of diesel and natural gas, even with lower hydrogen costs. Fig. 4 Fig. 4 Close modal Table 6 Case input data for the economic analysis CaseMethodNumber of refueling stationsNumber of trucksCapital per station (MCAD$)Cost of hydrogen (CAD$/m3) 1Constant Traffic Method4470,000472.2226.32 24470,000472.2261.93 34470,000472.2298.08 4Variable Traffic Method8170,0004.6226.32 58170,0004.6261.93 68170,0004.6298.08 CaseMethodNumber of refueling stationsNumber of trucksCapital per station (MCAD$)Cost of hydrogen (CAD$/m3) 1Constant Traffic Method4470,000472.2226.32 24470,000472.2261.93 34470,000472.2298.08 4Variable Traffic Method8170,0004.6226.32 58170,0004.6261.93 68170,0004.6298.08 Table 7 Tax-free and taxed selling price results of hydrogen from economic analysis on different cases CaseTechnical methodPurchase price of H2 ($/DLE)Tax-free selling price of H2 ($/DLE)Difference from tax-free selling price of diesel (%)Taxed price of H2 ($/DLE)
Diesel0.8230.01.2
1Constant traffic1.64762.572212.73.730
21.90692.930256.14.249
32.17003.293300.34.775
4Variable traffic1.64762.278176.93.303
51.90692.636220.33.821
62.17002.999264.54.348
CaseTechnical methodPurchase price of H2 ($/DLE)Tax-free selling price of H2 ($/DLE)Difference from tax-free selling price of diesel (%)Taxed price of H2 ($/DLE) Diesel0.8230.01.2 1Constant traffic1.64762.572212.73.730 21.90692.930256.14.249 32.17003.293300.34.775 4Variable traffic1.64762.278176.93.303 51.90692.636220.33.821 62.17002.999264.54.348 ### 4.2 Sensitivity Analysis and Results. A sensitivity analysis was performed on the same previous six economic cases in order to conclude if other variables were important in the calculation of the final selling price of hydrogen. The sensitivity analysis provides an overview of the effects of the parameters on the net present value of the investment, selling price of hydrogen, benefit–cost ratio (BCR), and the payback period (PB). For each case, the project capital cost, annual income, annual expenses, and presumed interest rate (IR) were varied by ±20%, and the selling price of hydrogen was re-estimated. The different scenarios that are considered for the sensitivity analysis are provided in Table 8. Table 8 Sensitivity scenarios Sensitivity scenarioDeviation 1Baseline case 220% increase in capital cost 320% decrease in capital cost 420% increase in annual income 520% decrease in annual income 620% increase in annual expenses 720% decrease in annual expenses 820% increase in interest rate 920% decrease in interest rate Sensitivity scenarioDeviation 1Baseline case 220% increase in capital cost 320% decrease in capital cost 420% increase in annual income 520% decrease in annual income 620% increase in annual expenses 720% decrease in annual expenses 820% increase in interest rate 920% decrease in interest rate The annual cash flow, present values, and annual equivalent cost (AEC) can be estimated using the following equations [62]. The annual equivalent cost is a decisive factor to estimate the cost of hydrogen. It can be calculated as follows: $AEC=ICc×i1−(1+i)−n$ (8) The net present value (NPV) of all the cash flows can be estimated as follows: $NPV=(B−C)*(1−(1+i)−ni)−ICc$ (9) Benefit–cost ratio (BCR) is an indicator of the ratio of the present values of the benefit cash flows to cost cash flows. It is calculated as follows: $BCR=B(1−(1+i)−ni)C(1−(1+i)−ni)+ICC$ (10) The PB is the total years required for NPV to reach 0. It is estimated by solving the equation below: $NPV=0=(B−C)*(1−(1+i)−PBi)−ICc$ (11) The internal rate of return (IRR) is the critical rate of interest where NPV =$0. For an investment to be financially feasible, IRR has to be more than or equal to the interest rate. It is assessed by solving the formula below:
$NPV=0=(B−C)(1−(1+IRR)−nIRR)−ICc$
(12)

Negative cash flow signifies expenditure, and positive cash flow denotes benefits. The NPV = 0 at 20 years is fixed to be the payback period for this economic study.

Tables 914 in the  Appendix provide the results of investment criteria of all Cases 1–6 having each nine scenarios. Figures 510 present the selling price of hydrogen for the diverse cases based on the sensitivity analysis. Results indicate that variations in the study capital cost and interest rate do not modify the final selling price of hydrogen. On the other hand, increasing or decreasing the annual income or annual expenses raised or reduced the final selling price of hydrogen by the same factor, which is to be anticipated. The negative NPV implies that one is in debt on its investment. This means that rising annual benefit (AB) and reducing investment cost, annual benefit, and interest rate increase NPV. The tax-free selling prices of hydrogen are in the range of CAD$2.06–3.82 for Scenarios 1–3, and CAD$1.82–3.60–for Scenarios 4–6. The taxed selling prices are in the range of CAD$3.09–5.73 for Scenarios 1–3, and CAD$2.64–5.13 for Scenarios 4–6.

Fig. 5
Fig. 5
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Fig. 6
Fig. 6
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Fig. 7
Fig. 7
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Fig. 10
Fig. 10
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Table 9(a)

Investment criteria of all scenarios for Case 1

NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor
Scenario(CAD$)(%)(–)(%)(–) 106.01.306.012.30 2−4,155,360,000.06.01.303.914.23 34,155,360,000.06.01.308.910.48 435,646,044,941.76.01.6523.39.71 5−35,646,044,941.76.00.986.21 60.006.01.256.012.30 70.006.01.406.012.30 80.007.21.306.012.04 90.004.81.306.013.05 NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor Scenario(CAD$)(%)(–)(%)(–)
106.01.306.012.30
2−4,155,360,000.06.01.303.914.23
34,155,360,000.06.01.308.910.48
435,646,044,941.76.01.6523.39.71
5−35,646,044,941.76.00.986.21
60.006.01.256.012.30
70.006.01.406.012.30
80.007.21.306.012.04
90.004.81.306.013.05
Table 9(b)
ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE 10.00232.57243.7300 21,759,192.8332.57243.7300 3226,238,506.3172.57243.7300 42,916,863,872.8153.08694.4760 5−29,405,653,929.32.05802.9840 60.00233.02704.3891 70.00232.11793.0710 8267,404,843.5292.57243.7300 9768,701,872.8212.57243.7300 ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE
10.00232.57243.7300
21,759,192.8332.57243.7300
3226,238,506.3172.57243.7300
42,916,863,872.8153.08694.4760
5−29,405,653,929.32.05802.9840
60.00233.02704.3891
70.00232.11793.0710
8267,404,843.5292.57243.7300
9768,701,872.8212.57243.7300

BCR: benefit–cost ratio; IR: interest rate; IRR: internal rate return; NPV: net present value; PB: payback period.

Table 10(a)

Investment criteria of all scenarios for Case 2

NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor
Scenario(CAD$)(%)(–)(%)(–) 10.06.01.266.011.47 2−4,155,360,000.06.01.263.913.76 34,155,360,000.06.01.268.99.29 440,600,902,182.76.01.9924.74.21 5−40,600,902,182.76.00.956.21 60.006.01.216.011.47 70.006.01.336.011.47 80.007.21.266.011.61 90.004.81.266.011.87 NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor Scenario(CAD$)(%)(–)(%)(–)
10.06.01.266.011.47
2−4,155,360,000.06.01.263.913.76
34,155,360,000.06.01.268.99.29
440,600,902,182.76.01.9924.74.21
5−40,600,902,182.76.00.956.21
60.006.01.216.011.47
70.006.01.336.011.47
80.007.21.266.011.61
90.004.81.266.011.87
Table 10(b)
ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE 10.00202.93004.2485 21,676,808.3302.93004.2485 3215,643,566.2142.93004.2485 41,764,349,585.053.51605.0982 5−31,509,531,698.52.34403.3988 60.0203.45605.0113 70.0202.40403.4858 8254,882,049.1262.93004.2485 9732,702,915.5182.93004.2485 ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE
10.00202.93004.2485
21,676,808.3302.93004.2485
3215,643,566.2142.93004.2485
41,764,349,585.053.51605.0982
5−31,509,531,698.52.34403.3988
60.0203.45605.0113
70.0202.40403.4858
8254,882,049.1262.93004.2485
9732,702,915.5182.93004.2485
Table 11(a)

Investment criteria of all scenarios for Case 3

NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor
Scenario(CAD$)(%)(–)(%)(–) 10.06.01.236.011.47 2−4,359,520,000.06.01.233.913.76 34,359,520,000.06.01.238.99.29 445,835,056,264.76.01.9726.84.21 5−45,835,056,264.76.00.946.21 60.006.01.196.011.47 70.006.0%1.306.011.47 80.007.21.236.011.61 90.004.81.236.011.87 NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor Scenario(CAD$)(%)(–)(%)(–)
10.06.01.236.011.47
2−4,359,520,000.06.01.233.913.76
34,359,520,000.06.01.238.99.29
445,835,056,264.76.01.9726.84.21
5−45,835,056,264.76.00.946.21
60.006.01.196.011.47
70.006.0%1.306.011.47
80.007.21.236.011.61
90.004.81.236.011.87
Table 11(b)
ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE 10.00203.29304.7749 21,676,808.3303.29304.7749 3215,643,566.2143.29304.7749 43,611,630,356.053.95165.7298 5−34,232,761,049.82.63443.8199 60.00203.89165.6429 70.00202.69443.9069 8254,882,049.1263.29304.7749 9732,702,915.5183.29304.7749 ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE
10.00203.29304.7749
21,676,808.3303.29304.7749
3215,643,566.2143.29304.7749
43,611,630,356.053.95165.7298
5−34,232,761,049.82.63443.8199
60.00203.89165.6429
70.00202.69443.9069
8254,882,049.1263.29304.7749
9732,702,915.5183.29304.7749
Table 12(a)

Investment criteria of all scenarios for Case 4

NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor
Scenario(CAD$)(%)(–)(%)(–) 10.06.01.006.011.47 2−74,520,000.06.01.003.913.76 374,520,000.006.01.008.99.29 431,563,813,518.76.01.51747.31.83 5−31,563,813,518.76.00.806.21 60.06.01.006.011.47 70.06.01.016.011.47 80.07.21.006.011.61 90.04.81.006.011.87 NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor Scenario(CAD$)(%)(–)(%)(–)
10.06.01.006.011.47
2−74,520,000.06.01.003.913.76
374,520,000.006.01.008.99.29
431,563,813,518.76.01.51747.31.83
5−31,563,813,518.76.00.806.21
60.06.01.006.011.47
70.06.01.016.011.47
80.07.21.006.011.61
90.04.81.006.011.87
Table 12(b)
ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE 10.0202.27783.3029 230,071.0302.27783.3029 33,867,236.2142.27783.3029 44,732,228,988.922.73343.9634 5−17,259,464,432.11.82232.6423 60.0202.73233.9619 70.0201.82332.6439 84,570,918.1262.27783.3029 913,139,901.5182.27783.3029 ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE
10.0202.27783.3029
230,071.0302.27783.3029
33,867,236.2142.27783.3029
44,732,228,988.922.73343.9634
5−17,259,464,432.11.82232.6423
60.0202.73233.9619
70.0201.82332.6439
84,570,918.1262.27783.3029
913,139,901.5182.27783.3029
Table 13(a)

Investment criteria of all scenarios for Case 5

NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor
Scenario(CAD$)(%)(–)(%)(–) 10.06.01.006.0%11.47 2−74,520,000.06.01.003.9%13.76 374,520,000.06.01.008.9%9.29 436,520,062,182.76.01.51863.3%1.83 5−36,520,062,182.76.00.806.21 60.06.01.006.0%11.47 70.006.01.016.0%11.47 80.007.21.006.0%11.61 90.004.81.006.0%11.87 NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor Scenario(CAD$)(%)(–)(%)(–)
10.06.01.006.0%11.47
2−74,520,000.06.01.003.9%13.76
374,520,000.06.01.008.9%9.29
436,520,062,182.76.01.51863.3%1.83
5−36,520,062,182.76.00.806.21
60.06.01.006.0%11.47
70.006.01.016.0%11.47
80.007.21.006.0%11.61
90.004.81.006.0%11.87
Table 13(b)
ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE 10.0202.63553.8215 230,071.0302.63553.8215 33,867,236.2142.63553.8215 45,524,453,258.023.16264.5858 5−19,942,768,152.62.10843.0572 60.0203.16154.5842 70.0202.10953.0588 84,570,918.1262.63553.8215 913,139,901.5182.63553.8215 ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE
10.0202.63553.8215
230,071.0302.63553.8215
33,867,236.2142.63553.8215
45,524,453,258.023.16264.5858
5−19,942,768,152.62.10843.0572
60.0203.16154.5842
70.0202.10953.0588
84,570,918.1262.63553.8215
913,139,901.5182.63553.8215
Table 14(a)

Investment criteria of all scenarios for Case 6

NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor
Scenario(CAD$)(%)(–)(%)(–) 10.06.01.006.011.47 2−74,520,000.06.01.003.913.76 374,520,000.06.01.008.99.29 441,550,056,264.76.01.51980.91.83 5−41,550,056,264.76.00.806.21 60.06.01.006.011.47 70.06.01.006.011.47 80.07.21.006.011.61 90.04.81.006.011.87 NPV with assumed interest rate and assumed lifecycleIRBCRIRR so NPV = 0 after 20 yearsNPV factor Scenario(CAD$)(%)(–)(%)(–)
10.06.01.006.011.47
2−74,520,000.06.01.003.913.76
374,520,000.06.01.008.99.29
441,550,056,264.76.01.51980.91.83
5−41,550,056,264.76.00.806.21
60.06.01.006.011.47
70.06.01.006.011.47
80.07.21.006.011.61
90.04.81.006.011.87
Table 14(b)
ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE 10.0202.99854.3478 230,071.0302.99854.3478 33,867,236.2142.99854.3478 46,328,465,254.823.59825.2174 5−22,665,997,503.92.39883.4783 60.0203.59715.2159 70.0202.39993.4798 84,570,918.1262.99854.3478 913,139,901.5182.99854.3478 ScenarioNPV with assumed interest rate and calculated PB (CAD$)PB using assumed IR (years)H2 tax-free selling price CAD$/DLEH2 taxed selling price CAD$/DLE
10.0202.99854.3478
230,071.0302.99854.3478
33,867,236.2142.99854.3478
46,328,465,254.823.59825.2174
5−22,665,997,503.92.39883.4783
60.0203.59715.2159
70.0202.39993.4798
84,570,918.1262.99854.3478
913,139,901.5182.99854.3478

## 5 Conclusions

The objective of this study was to investigate the viability of setting up a nationwide network of hydrogen refueling stations to assist in switching LHHD fleet from diesel fuel to hydrogen. Two methods based on constant and variable traffics were created to evaluate the capacity and number of refueling stations required for different levels of fleet conversion. Subsequently, two detailed economic studies were performed on six technical cases, to assess the final selling price of hydrogen, with subsequent sensitivity analysis on each case. The prices predicted by the study were compared to the current cost of diesel.

Results from the techno-economic analysis revealed that hydrogen prices can vary significantly, from about CAD$2.3/DLE to CAD$3.3/DLE, practically only contingent on the price of hydrogen, compared to CAD$0.82/DLE for diesel. On average, hydrogen is 239% more expensive than diesel. This is due to the diverse costs related to various production methods for hydrogen, and these results showed a rise in hydrogen of CAD$0.05/DLE for every CAD$1/GJ rise in hydrogen. It should also be mentioned that the data gaps on capital costs for hydrogen refueling stations constrain the precision of the assessments. Future work will concentrate on green hydrogen production potential and the effect of the implementation of various types of production processes on the cost to sell hydrogen to vehicle refueling stations. An analysis of the capital costs of larger-scale hydrogen refueling stations, as opposed to the current capacities being studied, would greatly increase the accuracy of the results found in this study. The methodology proposed in this feasibility study might be used for prospect techno-economics of hydrogen refueling infrastructure for other ICE vehicles. It is expected that these results help to propose basic rules and guidance in the assessment of hydrogen refueling stations for other ICE and fuel cell vehicle fleets or those powered with renewable fuels. ## Acknowledgment Funding for this work was provided by Natural Resources Canada through the Program of Energy Research and Development. ## Conflict of Interest There are no conflicts of interest. ## Nomenclature • i = rate of return (%) • • n = number of years since the initial investment (years) • • x = capacity of the refueling station (kg) • • y = capital cost of a refueling station (M$)

•
• B =

total investment ($) • • C = annual cost per year ($/year)

•
• I =

annual income ($/year) • • dd = distance traveled by a truck per day (km/truck/day) • • dh = total distance of highway taken into consideration (km) • • ds = distance between refueling stations (km) • • nd = number of days an LHHD truck is driving per year (days/year) • • nmax = lifecycle of the infrastructure (years) • • nrs = number of refueling stations required (–) • • nt = fleet size (–) • • rV = volume ratio of hydrogen to diesel (L H2/L diesel) • • ti = time spent per truck per day idling (h/day) • • Vfc = total volume of hydrogen consumed (m3/day) • • $V˙i$ = fuel consumption rate while idling (DLE/h) • • Vrs = volume of hydrogen each refueling station can hold • • Vt = volume of hydrogen truck’s fuel tank (m3) • • H2 = hydrogen • • AC = annual cost ($)

•
• CCS =

carbon capture and storage

•
• CNG =

compressed natural gas

•
• CIDI =

compression ignition direct injection

•
• CO2 =

carbon dioxide

•
• E-85 =

ethanol fuel blend of 85% ethanol fuel and 15% gasoline or other hydrocarbon by volume

•
• FFV =

flexible-fuel vehicle

•
• $FT%$ =

percentage of fuel from a truck’s fuel tank that is required to reach the next hydrogen refueling station (%)

•
• GHG =

greenhouse gas emission

•
• ICc =

initial investment ($) • • MCAD = million Canadian dollars • • NOx = nitrogen oxides • • WTW = well-to-wheels • •$ F =

final selling price (\$)

•
• ηf =

fuel economy of hydrogen engines (L/km)

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