Assessment of Energy Footprint of Pure Hydrogen-Supplied Vehicles in Real Conditions of Long-Term Operation

Abstract

The desire to maintain CO₂ concentrations in the global atmosphere implies the need to introduce ’new’ energy carriers for transport applications. Therefore, the operational consumption of each such potential medium in the ’natural’ exploitation of vehicles must be assessed. A useful assessment method may be the vehicle’s energy footprint resulting from the theory of cumulative fuel consumption, presented in the article. Using a (very modest) database of long-term use of hydrogen-powered cars, the usefulness of this method was demonstrated. Knowing the energy footprint of vehicles of a given brand and type and the statistical characteristics of the footprint elements, it is also possible to assess vehicle fleets in terms of energy demand. The database on the use of energy carriers, such as hydrogen, in the long-term operation of passenger vehicles is still relatively modest; however, as it has been shown, valuable data can be obtained to assess the energy demand of vehicles of a given brand and type. Access to a larger operational database will allow for wider use of the presented method.

1. Introduction and Literature Review

The challenge of the modern age is to achieve carbon neutrality. Transport is one of the economic sectors from which carbon emissions originate (e.g., operational CO₂ emissions from transport are estimated at approximately 17% of total emissions).

The basic energy carriers in transport are hydrocarbons.

The use of hydrocarbon energy carriers causes emissions during the acquisition of the carrier and its use. When obtaining carriers, it is necessary to use primary energy (especially electricity). The production of such energy usually leads to emissions. These emissions can be minimized by obtaining energy exclusively from renewable resources, but so far this has not been achieved anywhere. On the other hand, even assuming that no emissions are produced in obtaining the hydrocarbon carrier, by definition, such emissions will arise in the process of its use.

The use of hydrocarbons in the operation of means of transport causes emissions of carbon dioxide (CO₂) and water (H₂O). We already know that carbon dioxide emissions increase the concentration of CO₂ in the atmosphere and lead to a dangerous greenhouse effect.

The elimination of this threat can be achieved by obtaining energy from renewable resources and using it in the following ways:

• directly, e.g., as electricity,
• indirectly, in obtaining emission-free energy carriers, such as hydrogen.

Taking into account the above, and especially the fact that it is possible to completely eliminate CO₂ emissions, the development of the use of hydrogen technologies should be anticipated.

In discussions about reducing operational CO₂ emissions, it is believed that a ’transition period’ is necessary. It is believed that ’new’ energy carriers (e.g., hydrogen) should be introduced into conventional vehicles during this period.

Energy carriers include both electricity and flammable liquids, including fuels. Fuels are, of course, classified as flammable liquids, but they are distinguished by legal regulations, i.e., the properties of fuels are described in appropriate standards so that their use is safe in engines or fuel cells, among others.

Hydrogen is a flammable liquid, but for legal reasons, it is not currently used as a fuel in every country.

An analysis of the literature on the use of hydrogen allows us to conclude that, so far, little attention has been paid to the issue of formally recognizing hydrogen as a fuel (the fuel parameters of hydrogen are not standardized), although other issues related to hydrogen are interesting.

However, views on the use of hydrogen as a fuel may be completely different.

The authors of the work [1] believe that the desire to use hydrogen “is cyclical”. Excessive expectations were followed by disappointment with hydrogen technologies. However, they believe that there is growing evidence that these technologies will nevertheless be an attractive option for the deep decarbonization of the world’s energy systems. The achieved cost and efficiency improvements also indicate their possible economic viability. However, after carefully analyzing the issue, they concluded that cost and performance challenges remain and that significant improvements in hydrogen technologies are still needed. These challenges can be overcome in the medium term, which, in their opinion, fully justifies the growing interest and political support around the world for hydrogen technologies.

The authors of the study [2] concluded that previous predictions regarding the role of hydrogen in transport were too optimistic. Even if the world eventually responds seriously to climate change, hydrogen will not be the preferred fuel for passenger cars for many decades. However, they stated (without analytical evidence) that hydrogen-powered vehicles have a clear advantage over electric vehicles in heavy freight transport.

In the study [3], the authors looked at improving the energy efficiency of hydrogen FCEVs in a chain involving policy, H2 suppliers, FCEV manufacturers and customers. It was found that the demand for FCEV increases with the increase in the efficiency improvement index. Moreover, the demand for hydrogen cars can be artificially increased by increasing the subsidy rate and reducing the tax rate.

The study [4] provides a scientific reference for policies aimed at expanding the use of hydrogen in the (Chinese) emissions trading system. The development of logistics and heavy-duty FCEVs should be a priority. The use of emission allowances could almost offset half of the current costs of the hydrogen supply in the future (approximately USD 12.48/kgH₂). This means that the development of the use of hydrogen must be politically supported.

Taking into account the conflicting views on the use of hydrogen to power vehicles, a meta-analysis was presented in [5] which used 24 literature items to systematically assess factors influencing consumers’ willingness to purchase HFCV vehicles. The article presents new research methods and, on this theoretical basis, refers to the future government promotion of hydrogen FCEVs. It was found that the purchase of FCEVs is positively influenced by knowledge, density of hydrogen refueling stations and political incentives, while the negative factors mentioned are household size, purchase price, fuel cost, maintenance cost and refueling time.

Overall, LCAs [6] confirm that FCEVs powered by H₂ from renewable sources provide minimal greenhouse gas emissions, even better than BEVs. However, the conclusions from EROI research are less optimistic because wind and solar sources produce only 3–11 times more energy than the energy used for their production, installation and operation. However, everything depends on the resources of exploitation of these sources.

The next article is treated as a contribution to explaining the sense of using H2 as an energy carrier in transport. Ref. [7] presents GHG emissions from 19 FCEV use paths in China. Based on LCA, it was shown that the highest GHG emissions occur when H₂ is obtained from water electrolysis (3.10 kgCO₂/km), while it is the lowest when H₂ comes from the chlor-alkali industry (0.08 kgCO₂/km). When it comes to hydrogen storage and transport, the use of compressed hydrogen gas provides the lowest GHG emissions in road transport. Interestingly, GHG emissions from DMFCEVs (CH₃OH-powered) are lower than from FCEVs (H₂-powered).

The results of the study [8] show that the production of H₂ from water in the electrolytic process but using energy from renewable resources is characterized by the lowest emissions, including GHG, while the traditional production of hydrogen from natural gas, coke oven gas, coal, etc., has better economic results. However, both emissions and costs of FCEVs are expected to fall by 2030. Moreover, the article postulates the need to apply appropriate policies to intensify and cheapen the production of H₂ from renewable resources.

The authors of [9] discussed the issue of the use of H₂ more broadly, presenting a road map of hydrogen energy and the FCEV industry in the USA, Japan and the EU. It was concluded that the (Chinese) hydrogen energy and FCEV industry has started to diversify but is still not sustainable. The place of hydrogen energy in the energy structure (of China) should be clarified by the government, which should also determine the development path and scope of implementation of hydrogen energy. In the future (in China), expenditure on research and development of technology and the creation of infrastructure should be increased, and research and development institutions for the hydrogen and FCEV industries should be established. The development of advanced technologies should be financed, and the construction of infrastructure for the production, storage, transport and refueling of hydrogen should be intensified in order to promote the development of the H₂ industry. Comparing the FCEV industry in different countries, it seems that these postulates can also be applied in other countries.

The authors of [10] rightly noted that before the FCEV is popularized, it is necessary to forecast the demand for hydrogen. The demand for 2040 was estimated using Bass and Gompertz. It is expected that the daily demand for H2 from one hydrogen station will range from 1.0 to 2.3 Mg. This relatively large difference in demand can be explained by large differences in cumulative H2 consumption (discussed later in the article).

In line with previous accounts, some more general results were presented in [11]. Here, LCA was determined by taking into account key parameters of FCEV production and various methods of obtaining hydrogen. The use of H2 from photovoltaic-powered electrolysis has been shown to reduce global warming potential (GWP) by approximately 76.4% compared to steam reforming of methane (from natural gas). What is important, however, is that obtaining H2 from electrolysis powered by energy from today’s standard power grid (China’s in this work) increases GWP by approximately 158.3%.

The authors of [12] also explored LCA for FCEV. It was found that fuel cell degradation negatively affects the vehicle’s average fuel consumption (an increase of approximately 23%).

In the work [13], an LCA model of primary energy consumption and GHG emissions in H2 supply chains for FCEVs (in China) is presented. Primary energy consumption is the lowest when using electricity from hydropower and nuclear power plants (from 0.48 to 0.94 MJ/MJH₂). There are also GHG emissions. However, when obtaining hydrogen from natural gas, GHG emissions over the entire life cycle are 187–235 g CO₂, eq/km, so they are similar to those when using conventional fuels. FCEVs powered by H₂ obtained using electricity from the current grid emit, on the LCA, two- to three-times more GHG than combustion engine vehicles. According to the authors, FCEVs have no obvious advantages in terms of primary energy consumption and GHG emissions compared to BEVs and ICEVs—which may be surprising.

The authors of [14] compared energy consumption and source-to-wheel (WTW) emissions using GREET software and performed an LCA for four vehicle types. The lowest energy consumption was demonstrated for BEV. Comparing nine types of air pollutant emissions from four types of vehicles, FCEV emissions are significantly lower than those of ICEVs (SI ICEV and DI ICEV). Surprisingly, FCEV emissions are lower than those of BEVs. Does the electricity to produce and distribute H2 come from sources other than the electricity to power the BEV? It seems that the authors’ conclusion was formulated without taking into account the actual energy demand for the long-term operation of vehicles.

The methodologically sound study [15] critically reviewed and analyzed the latest technologies for H₂ production, storage and distribution. Costs and GHG emissions were taken into account. Numerous knowledge gaps were identified and future research directions were provided. The authors conclude that hydrogen production costs using established technologies are competitive with the DOE target, but they have significant GHG emissions, which runs counter to the net-zero goals. This is where renewable energy-based electrolysis may hold promise in the long term. Referring to overall costs, the authors hope for the so-called ‘scale effect’. It is believed, for example, that the levelized cost of hydrogen at a refueling station can be reduced by increasing the station utilization rate, increasing the production of station equipment and reducing the costs of obtaining hydrogen for an increased fleet. It is believed that portable refueling stations have great potential for small fleets. When it comes to storing hydrogen in transport, materials such as activated carbon, carbon nanotubes and nanofibers show promising ability to store hydrogen but under cryogenic conditions. On the other hand, metal hydrides have the ability to absorb hydrogen at ambient temperature (but they can absorb relatively little hydrogen in relation to their mass). Temperature problems during refueling, especially temperature increases, can be solved by the appropriate control of the H2 flow rate and by pre-lowering the inlet temperature and refueling at a graded initial pressure.

The previous study is supplemented by the work [16] which discusses the latest achievements of FC technology from the perspective of the automotive industry and presents current bottlenecks hindering the commercialization of FCEVs. Issues such as high manufacturing and maintenance costs of FC stacks, insufficient H2 delivery infrastructure, slow cold starts, reliability and safety issues and immature energy management systems in FCEVs are expected to be addressed.

The review [17] presents the current technological progress and the application status of FC in the automotive industry. Attention was paid to factors hindering the development and commercialization of FC technology. Suggestions of possible solutions to reduce the cost of FC cells and their applications in the automotive industry are presented. The article presents interesting issues but does not include an energy analysis.

According to the authors of [18], FCs are efficient for energy conversion. The article analyzes different types of FC. Their various environmental aspects were discussed in detail. To illustrate the relative environmental benefits of FCs, the review compares them to conventional power generation systems.

PEMFC, which was explored in the work [19], uses H₂ as a fuel, achieving an energy efficiency of 65%; however, it is expensive. The electrocatalyst itself, made of platinum group metals (PGM), accounts for about 40% of the cost of the FC stack.

After several decades of research on fuel cell systems (FCS), interest in them is decreasing, and the possibility of using them in transport is questionable (according to the authors of [20]). The aim of the article was to look at this topic from the perspective of road transport. The hope lies in fleets of vehicles that can be developed with a new drive architecture that increases their range. As a result, the hydrogen sector could improve the required technology and move from niche markets to large-scale markets.

In search of the reasons for the slight increase in interest in FCEVs, the article [21] presents a set of economic factors influencing future purchasing decisions regarding this category of vehicles. A numerical experiment was carried out. The authors emphasize the important role of the total cost of ownership (TCO) indicator—which, in our opinion, is an indispensable component of the cost of fuel in long-term operation—which, without knowing the appropriate method of determining it (presented in this article) can only be assessed superficially.

More general analysis regarding the introduction of hydrogen as an energy carrier can be found, among others, in the publications listed below.

Alternatives to diesel vehicles include BEVs and FCEVs [22]. A detailed comparison of three types of heavy vehicle drive systems is presented. Overall, conventional propulsion may exist in the short term due to existing infrastructure and lower costs despite high emissions, while BEV and FCEV truck technologies will slowly develop but overcome cost barriers and infrastructure underdevelopment.

FCEVs powered by H₂ from renewable sources may be suitable for decarbonization of transport according to the authors of [23]. This issue was examined as case studies of potential renewable H₂ projects in California. A 100% and 75% share of renewable energy in H₂O electrolysis was assumed, respectively. In the LCA of fuels and vehicles, the carbon footprint in the FCEV life cycle was determined. It amounted to 105 to 149 g CO₂ equivalent/vehicle km, which is comparable to the carbon footprint of BEVs charged from the Californian network (132 g CO₂ equivalent/vehicle km) and half that of ICEVs. Electricity from renewable resources is the decisive factor in carbon footprint LCAs, which ranges from 1.5 to 6.0 kg CO₂ equivalent/kgH₂. FCEVs powered by H2 from renewable sources can contribute to achieving decarbonization goals in transport.

The latest pre-production vehicles (presented in [24]) available on the market show that the main technical challenges posed by the integration of a fuel cell system (FCS) in a vehicle—compactness, safety, autonomy, reliability and cold start—have been met. It is believed that FCEVs will play a key role in the path to zero emissions within one or two decades (this conclusion has been repeated many times in the past decades) and further, the greener the electricity, the greater the advantage of hydrogen—especially over vehicles powered using fuels from non-renewable resources.

The authors of [25] report that despite the long history of hydrogen-powered vehicles and FCEVs, economic performance is perceived as a key barrier to their development. The main conclusion of the work is that to achieve the full benefits of FCEV in the transport sector, it is necessary to harmonize activities between regions, especially political ones, to take advantage of economies of scale.

The article [26] presents an in-depth analysis of the current state and future potential of FCEVs. According to the authors, despite impressive energy efficiency ratio (EER), higher power-to-weight ratio and significant emission reduction potential, widespread implementation of FCEVs is currently hampered by high production costs, low H₂ energy density, in addition to safety issues and FC durability. They also add insufficient refueling infrastructure and the complexity of storing and transporting H₂. However, the energy costs of obtaining H₂ are not included in this list, which is puzzling.

The authors of [27] noted that after the successful adoption and implementation of BEVs in Norway with the support of generous government incentives, several other countries have started to introduce policy incentives for environmentally friendly vehicles. Japanese consumers’ preferences for FCEVs were found to be much lower than for ICEVs, and a policy incentive package could change this.

Ref. [28] presents a novel study of the differences in total cost of ownership and marginal abatement cost (LCA) of both new and used light-duty FCEVs and ICEVs (in Japan). Cumulative GHG emissions decline rapidly when FCEVs exceed 55–70% LDV. The key factors to ensure benefits are FC cost and electrolyzer efficiency. Interestingly, the authors’ prediction is that if the number of FCEVs approaches 50% by 2030, it will be possible to achieve the 2050 emission reduction target of 80% compared to the 2013 baseline.

According to the authors of [29], until FCEV technologies achieve an acceptable level of costs, efficiency, reliability, durability and safety, car manufacturers and further car buyers will not invest in this technology. According to the authors, on-board H2-producing subsystems must compete with what was previously available in modern ICEVs.

Moving on to technical considerations, it is worth paying attention to the previously mentioned so-called ’drive architecture’, especially since FCEVs with hybrid drives are proposed, where fuel cells are supplemented with batteries and, more recently, supercapacitors.

The article [30] summarizes strategies for optimizing energy management in FCEVs in terms of energy achievement and FC durability for the first time. Energy management strategies (EMS) that rely on predictive driving information technologies were analyzed.

In the study [31], an energy comparison between FCEV and hybrid FCEV (FCEVH) is presented. FCEVH is equipped with a traction battery (15 kWh). The AVL Cruise program was used for the model. The numerical analyses were illustrated with Sankey diagrams. WLTP was selected as the valid driving cycle. There was a decrease in the average H₂ consumption by FCEVH in WLTP of approximately 32%.

Ref. [32] presents six different drive system architectures that were mathematically modeled and analyzed in relation to real road conditions. The hybrid case involved the use of a CVT transmission. With such a gearbox, it is possible to reduce energy demand.

In the study [33], the power and range of vehicles with various FCEV drive architectures were analyzed. According to the authors, the results obtained prove that FCEVs may be a feasible and effective solution for the future (however, no time horizon is provided here).

The aim of the study [34] was to comprehensively compare different energy management strategies in FCEVH (including supercapacitors). Two innovative strategies for using the salp swarm algorithm (SSA) were presented, which turned out to be the most promising.

The use of ultra-capacitors in FCEVs may contribute to their development by significantly extending the service life of FCs [35]. The energy management of FCEVHs equipped with ultra-capacitors and batteries was analyzed to find a trade-off between hydrogen consumption and the number of start/stop cycles. Research shows that a predictive power management strategy can significantly reduce FC degradation without compromising system performance (i.e., without minimizing H2 consumption).

The drive architecture to reduce H2 consumption and increase the service life of FCEVs [36] should be multi-stage (MS). It is necessary to design a new energy management strategy (EMS) for such a structure. Experimental data show that FCEVs with an MS structure achieve lower hydrogen and degradation costs than with a single-layer structure (SS); however, no indication is given whether, e.g., the reduction in H2 consumption is significant.

The article [37] discusses the key challenges associated with the use of FCEVs. The operational properties and applications of several FC technologies in FCEVs and FCEVHs were investigated. The power transformation of FCEVHs has been studied analytically. The latest technological developments and the potential prospects for FCHEV achieving zero emissions (?) were discussed.

The work [38] presents the FCEV model, its validation and comparison of various vehicle control strategies (Toyota Mirai first-generation). The main parameters tested are H2 consumption and battery SOC variability. The FCEVH model is simulated in the MATLAB^® Simulink environment. The model results were verified with experimental data from the ANL (Argonne National Laboratory) database. H2 consumption minimization has been implemented in the controller logic. The ECMS control strategy was found to outperform the rule-based strategy by 0.4–15.6%, but it performed worse in some driving cycles and better in others.

The article [39] considered the optimal selection of an FCEVH drive system consisting of a compact reformer, a compact battery and a low-temperature FC PEM stack. A simulation model of the system was developed. Significant reductions in vehicle weight and fuel consumption can be expected with more modest performance and lower auxiliary energy requirements.

The article [40] presents the optimization of the FCEVH drive system. The goal is to minimize H2 consumption. The vehicle’s drive system consists of PEM FC, SC supercapacitors and a DC electric motor. SC is an energy buffer during the peak load of the electric motor and FCs operate quasi-stationarily. In this way, the FC can achieve higher efficiency and H2 consumption is minimized. The good results obtained were confirmed by experimental tests in the laboratory and on the race track.

Ref. [41] analyzed fuel consumption and emissions data collected from NEDC and WLTC simulations for ICE, FC and FC + ICE engine vehicles and compared them using a MATLAB^® Simulink model. The use of FC + ICE has been found to provide fuel savings and emissions can be zero only when FC is used (obviously).

The article [42] focused on energy management in an ultra-energy-saving FCEVH with supercapacitors with a rated power of 1 kW. A simulation model was developed. Contrary to expectations, FCEVH hydrogen consumption was 8.1% higher compared to FCEV.

The paper [43] presents a study of energy management in heavy-duty FCEVs. An important and unique contribution of the authors is the development of data from realistic vehicle operation, which includes 1750 h of driving under variable load conditions. It has been shown that in order to test energy management strategies, it is necessary to consider a number of realistic driving scenarios.

In [44], a Fuel Consumption Minimization Strategy (FCFCMS) is presented for an FCEVH with an energy storage system. The goal was to minimize H₂ consumption while maintaining battery SOC. The FCEVH model was built in TruckMaker/MATLAB^®. The effectiveness of the proposed strategy was compared using the urban driving test (UDDS); additionally, using fuzzy logic, a reduction in hydrogen consumption was achieved.

The operation of the FCEVH was simulated when the drive system consisted of (only) two energy sources: a fuel cell and a supercapacitor [45]. Simulations were performed using MATLAB^®/Simulink software. The control strategy tested in various driving cycles demonstrates the ability to minimize H₂ consumption.

Various tests are a separate issue (especially with the use of AI).

The article [46] focuses on FCEVHs, which optimally combine the FC system with batteries and ultra-capacitors. The latest proposed FCEVH topologies are compared and new technologies and DC/DC converters are discussed. Three types of strategies are discussed, i.e., rules, optimization and AI-based strategies. New AI algorithms need to be developed in the future.

A strategy based on dynamic programming optimization was developed to improve fuel efficiency and durability of vehicle systems [47]. Simulation results show that hydrogen consumption per 100 km in the strategy based on dynamic programming optimization is reduced by 6.46%.

The degree of energy use in the vehicle operation process is presented in the article [48]. An operational energy management strategy based on the Pontryagin minimum principle (PMP) was proposed. Based on the simulation results, it was found that the energy conservation rate is about 5% higher than the unoptimized one, which may contribute to lower hydrogen consumption.

Taking into account the subject of our publication, it is important to follow the methods for assessing hydrogen consumption.

In the study [49], to investigate fuel consumption in the operational phase of FCEV, two typical FCEV passenger cars were compared and analyzed. Hydrogen consumption and emissions were examined under two operating conditions: at different power levels in steady state mode and in the Chinese cycle carried out on a chassis dynamometer. The aim of this study was to provide (China) with reference data to study the economics of the operational phase of FCEVs.

There are few quantitative studies on hydrogen consumption during natural vehicle operation. Therefore, a research method based on hydrogen consumption of FCEV under CLTC-P operating conditions was proposed to determine fuel consumption (kg/100 km) [50]. According to the authors, based on this method, hydrogen consumption can be accurately measured and the time and cost of the test can be effectively shortened.

In the paper [51], a new methodology for assessing FCEVH consisting of FC + SC is presented. According to this methodology, the simulation results in the urban environment of the RDE test are optimized. It has been demonstrated that an average reduction of 37% can be achieved in energy consumption in the urban environment of the RDE test

Tests of the FCEVH vehicle were carried out in typical road traffic conditions [52] in accordance with the requirements of the RDE test. A large variation in the share of FC and BAT in vehicle propulsion was found. The share of FC is more than 3-times greater than BAT in urban conditions, 7-times greater in suburban conditions and 28-times greater on highways. The FC/BATT energy consumption ratio was over 7.

It is also interesting to compare classic solutions with internal combustion engines (ICE) with BEV and FC.

Simulation studies [53] were carried out using the Simcenter Amesim software environment. The modeling method was used to determine each vehicle’s performance under New York City driving conditions. GREET software was implemented for LCA of vehicles. It was found that the CO₂ emissions of FCEVs compared to gasoline, CNG and BEVs were 75.87%, 73.42% and 35.5% lower, respectively—which is surprising for BEVs.

Similarly, the paper [54] describes the suitability analysis of the proposed hydrogen fuel cell bus (FCEV) in detail. The model (Simulink) was validated on standard driving cycles and several real bus routes with recorded data. An interesting finding is that FCEV causes a greater environmental burden than its BEV counterparts.

An LCA model was proposed in [55]. The Source Energy Consumption Ratio (SECR) has been proposed to evaluate the energy efficiency of vehicles. The results show that such a model can be useful for analyzing the energy efficiency of new vehicles.

The lack of demand for FCRV is due to, among others, the conclusion that strategies for generating demand for alternative energy carriers, such as H₂, require a thorough understanding of the preferences of potential customers [56]. The study shows that in order to eliminate bottlenecks in the development of hydrogen mobility, car and hydrogen manufacturers as well as research and financing institutions (plus politicians, of course) should be interested in the preferences of buyers.

An interesting example [57] presents the results of research on hydrogen-electric vehicles (HEV), but with a spark-ignition internal combustion engine powered by fuel with the addition of hydrogen. The results show that fuel enrichment with hydrogen caused fuel consumption and pollutant emissions to decrease by 12.6% and 14–33%, respectively. Of course, HEV with an internal combustion engine is only one possible solution [58]. Another more futuristic solution is the FCEV presented, for example, in [59]. It is important that the authors concluded that the results of road tests could be the basis for the future implementation of such optimized cars. In turn, the authors of [60] show that it is possible to reduce GHG emissions from a vehicle fleet to 13.9%, but after the introduction of FCEV.

However, there are often concerns that the introduction of hydrogen as an energy carrier in medium and heavy-duty vehicles (MHDV) may be problematic from many reasons.

In the case of MHDV, it is not clear whether replacing diesel is even an option, as CO₂ emissions from road freight transport appear to be secondary to emissions from passenger cars. Ref. [61] describes the development of a vehicle fleet data analysis method to investigate this problem. The results are from vehicles with fuel cell powertrains. As a side note, it is worth mentioning that it was estimated that if every Swiss truck was powered by H₂, achieved exclusively via water electrolysis, the full decarbonization of this mode of transport would consume over 13% of today’s national electricity consumption. Moreover, issues are raised about the durability and reliability of this type of drive resulting from the degradation of FC and the impact of this process on the profitability of MHDV operation [62].

Even such “distant” problems as the lack of space for hydrogen tanks (they have a very complicated structure) are raised in the discussions.

The authors of [63] suggest that in most MHDV vehicles there is enough space for the hydrogen collected in the tanks to be sufficient for 90% of the demand for the vehicle’s daily mileage.

The strategy of managing energy flows in the operation of means of transport in real time is also important.

The paper [64] presents an optimal real-time energy management strategy to optimize the daily operation of a PMFCEV intended for public transport. The simulation model showed that daily operating costs could be reduced by 6.4% (the only question is whether this will happen in the long-term operation of the vehicles).

The paper [65] investigates a FCEVH for a regional railway. The simulation results achieved, in power demand, efficiency and daily H₂ consumption (but not in long-term demand).

The article [66] draws attention to issues related to hydrogen transport, namely hydrogen refueling stations. Surprisingly, it was shown, using computational examples, that such a station can operate effectively in the power grid and lead to an increase in the renewable energy use rate from 48.0% to 84.6%.

Other considerations are also important, such as noise and efficiency, as for example presented in [67].

A summary of the issue of implementing H₂ in heavy transport is included, for example, in [68].

The study [68] reveals that the transformation of freight transportation would require a comprehensive multi-criteria assessment that includes technical, economic, environmental and social feasibilities over the life cycle of the freight vehicle and the fuel supply chain. Moreover, decision parameters affecting the optimal fuel selection process were established through this study, while providing insights on the future prospects for hydrogen-fueled freight transformation. This study provides background information on the transition of road freight transport from fossil fuels to hydrogen, followed by opportunities and challenges. While this is very good information, unfortunately (as most studies of this type), it does not take into account the problems of assessing fuel (hydrogen) consumption in long-term operation of vehicles. This makes it difficult to estimate the actual demand for this type of energy carrier and thus it is difficult to estimate the energy expenditure for obtaining hydrogen (in this case). As a consequence, it is difficult to estimate, for example, the size and efficiency of the installation for obtaining hydrogen (from renewable resources) and the ecological and economic costs of the entire project.

An interesting (and generalizable) simulation result is also presented in the publication [69]. The decarbonization of the operation of heavy-duty trucks was analyzed. The use of various drive systems was analyzed, including the use of hydrogen, hybrid and synthetic fuels. Seven different aspects were presented, from the total cost to aspects such as the need for standardization. It was found that although battery electric trucks are beneficial, the infrastructure requires high expenditure (with high financial risk).

The paper [70] presents real possibilities of reasonable use of BEVs and FCEVs. To guarantee a reliable supply of energy from 100% renewable sources, energy storage is required. Such a strategy was presented for five EU countries. A 50% share of BEVs and 50% of FCEVs connected to the grid was assumed. As a result of the simulation, it was found that the FCEV hydrogen consumption should be verified, which was assumed to be 0.60 kgH₂/100 km (for passenger cars).

The authors of [71] concluded that FCEV vehicles will be developed in the years 2030–2050, but with limited use of expensive technologies to increase the range of vehicles and increase the number of hydrogen refueling stations.

The results of the authors’ work [72] indicate that both drivers and non-drivers preferred BEVs over FCEVs. Further research is being conducted, the results of which will be used to formulate FCEV-related policies in the light of the expected (future) competition between BEVs and FCEVs.

The future of hydrogen fuel cells (HFC) and the future of H₂ production were presented in [73]. The expected social and economic effects in the coming years are presented. Vision 2050 was found to explain implementation trends in several sectors.

If we assume that FCEVs will play an increasingly important role in future transport [74], then we can assume that global research should be supported to make FCEVs the leading type of vehicle in our sustainable, developing world.

As can be seen from the above, views on the use of H₂ as an energy carrier are not clear.

There are no convincing arguments given. The basic arguments discuss the following:

• The energy amount for obtaining and delivering hydrogen on board the vehicle;
• The total hydrogen demand of a specific vehicle (and vehicle fleet) for long-term operation, i.e., for carrying out transport tasks.

Both arguments should be considered together.

This work is devoted to the latter issue, in particular the problem of how to determine the demand for wood for the long-term operation of a vehicle (and/or a fleet of vehicles).

This above led to the statement that there are two obvious questions today that have not yet been answered clearly:

• Which fuel will be the standard? (it seems that it will be hydrogen, but it is also possible to use fuel that is easier in terms of logistics, i.e., methanol (if the CO₂ for its production is derived from the atmosphere),
• How much of a certain type of fuel will be needed to cover the transport demand?

Although, as stated, there is no unequivocal answer to the above questions yet, the second question generates additional problems about how to assess the demand of new energy carriers.

It seems that the only reliable option in this case is the assessment of energy consumption during the so-called ’natural’, long-term use of a vehicle of a given brand and type. This assessment can be made by estimating the cumulative consumption of the energy carrier by the vehicle.

Views on how to assess a vehicle’s fuel consumption and how to minimize it also vary greatly.

In the study [75], a predictive equivalent consumption minimization strategy (P-ECMS) was proposed that uses velocity prediction and takes into account various dynamic constraints (to limit FC degradation estimated by a special sub-model). The proposed strategy can contribute to reducing H2 consumption in the range of 1.4% to 3.0%.

Ref. [76] is one of the first publications on FCEV fuel consumption. The authors came to the ’classic’ conclusions that the main factors influencing the reduction of vehicle fuel consumption are reducing its own weight, air resistance and increasing the efficiency of the drive system. A vehicle with an empty weight of 850 kg equipped with a PEFC/SUPERCAP hybrid drive system powered by H₂/O₂ was tested. H₂ consumption in NEDC, FCEVH was 0.67 kg H₂/100 km. After taking into account the energy needed to supply pure O₂, the calculated H₂ consumption increases from 0.67 to 0.69–0.79 kg H₂/100 km, depending on the oxygen production method.

In December 2009, Mercedes-Benz launched a fleet of 200 Class B F-Cell FCEVs [77]. The article describes interesting technical experiences resulting from the operation of this fleet of vehicles, which have traveled over 3.3 million kilometers so far, but does not provide more information about the consumption of H₂.

The development of cities has a significant impact on energy consumption in transport. According to the authors of [78], FCEVs can effectively reduce energy consumption and pollutant emissions in urban transport. This conclusion seems to be correct, but it would be good to provide a method for assessing the consumption of energy carriers.

The method of assessing the consumption of energy carriers is presented, for example, in [79]. A simple model is presented, using which, knowing the instantaneous vehicle speed, acceleration and road slope as decision variables, the instantaneous consumption of energy carrier is calculated. With respect to the short-term test data (approximately 1450 s), errors of 0.86% to 2.17% were determined. The authors concluded that out of a number of studies, only a few focused on adequate models of energy consumption.

Energy is necessary throughout the vehicle’s life cycle, i.e., in the processes of design, production, operation and disposal of the vehicle. The exploitation process generally consists of two sub-processes, i.e., the use sub-process and the service sub-process. In the use sub-process, it is necessary to use energy carriers, e.g., hydrogen. The method of assessing the demand for these media in the use sub-process is the subject of considerations in this publication.

The consumption of energy carriers in the ’natural’ operation of vehicles is the result of many variable factors which is why tests were introduced. Tests must be modified to suit changing operating conditions. Recently, for example, NEDC was replaced by WLTP [80,81,82,83,84]. It turned out that no significant improvement was achieved, i.e., data from natural exploitation usually differ significantly from the test data. Therefore, the use of the RDE test [85,86,87,88] or simulation [89] is becoming more and more common. The RDE test is used to test emissions [90], but it seems that it can also be used to assess the consumption of energy carriers.

The conclusion from the paper [91] is interesting. It was found that there are only few quantitative studies on H₂ consumption during vehicle operation. The main reason is, among others, the high cost of H₂ for testing. Therefore, a test method according to the CLTC-P test (Chinese light vehicle test—similar to WLTC) was proposed to examine H₂ consumption (FCEV) per 100 km. It was found that H₂ consumption can be determined quite precisely, in a shorter time and at lower costs.

Regardless of the results of the example solutions presented above, there are still discrepancies between the energy consumption determined in short tests (even in RDE-type road tests) and the consumption of energy carriers observed in the long-term, natural operation of vehicles. It seems that the solution to the problem of correct assessment of the operational demand for energy carriers can only be expected after the introduction of the theory of their cumulative consumption. The theory was presented, among others, in publication [92]. This publication will present only the necessary resulting mathematical equations.

The most frequently used assessment factor for analyzing fuel consumption is FE (Fuel Economy) expressed here, in the case for hydrogen consumption, in kg/100 km of mileage.

Assuming that;

F_i—i-th refueling,

t_di—mileage to F_i,

i-th fuel economy (FE_i) is:

FE_i = 100F_i (t_di − t_di-1)

(1)

After n refueling, the AFE (Average Fuel Economy) is:

$${AFE}_n={\displaystyle \frac{1}{n}}\sum _{i=1}^n{FE}_i$$

(2)

To apply Equations (1) and (2) (as well as further ones presented in this article), data from the natural operation of vehicles are necessary. Such data can be found, among other locations, in databases, including publicly available ones.

The operational data of the vehicles analyzed in this publication come from a publicly available website spritmonitor.de (accessed on 20 October 2022). This was performed deliberately so that everyone could verify the results obtained using the presented theory.

Each FCEV (in the database) has its own individual number (Figure 1). Operational data come directly from car users.

The database is presented in the form of an individual table for each vehicle. The table contains the date of refueling and the mileage up to the time of refueling, distance since the previous refueling, amount of fuel refueled, price of refueled fuel and FE after refueling. Additionally, an FE histogram is provided.

The table includes a pie chart of the percentage of vehicle operating conditions, i.e., the share of driving in the city, on non-urban roads and on highways. This article uses data regarding the mileage and the corresponding amount of fuel refueled. The method of using this data is presented below.

Figure 1 shows FE and AFE values for examples of hydrogen-powered cars.

FE values, despite relatively large dispersion, are generally arranged in a kind of wave pattern. This data picture can also be observed when vehicles are powered by other energy carriers. Attempts were made to correlate it, e.g., with the temperature values at which the vehicles were operated, but no convergence was observed (further research is required to explain this phenomenon).

AFE, in the case of operation of this particular car, is characterized by a downward trend.

No broader conclusions can be drawn from the data presented. It is necessary to use other, more advanced assessment methods. One such method is to determine the vehicle’s energy footprint.

2. Theory of Cumulative Fuel Consumption

If CFC is the cumulative fuel consumption and it is proportional to the vehicle mileage, then:

$$CFC=f\left(Mileage\right)$$

(3)

The CFC for the t_d course is described in [92] as follows:

$$CFC\left(t_d\right)=c{t}_d^{a+1}$$

(4)

where:

c, a—constants

t_d—the mileage.

A derivative of CFC is the ICFC (Intensity of Cumulative Fuel Consumption)

$$ICFC\left(t_d\right)={\displaystyle \frac{dCFC\left(t_d\right)}{{dt}_d}}=c{(a+1)t}_d^a$$

(5)

It is important is to know that by t_d = 0, the ICFC(t_d) does not exist.

For SCFC (Specific Cumulative Fuel Consumption—similar to the AFE), there exists a relationship

$$SCFC\left(t_d\right)={\displaystyle \frac{CFC\left(t_d\right)}{t_d}}=c{t}_d^a$$

(6)

The SCFC(t_d) is in kg/km; therefore, by multiplying this value by 100, it corresponds to the AFE.

CFC, ICFC and SCFC together constitute the energy footprint of a specific vehicle. Such a set of values can be determined for each analyzed vehicle.

An example of the application of the theory of cumulative energy carrier consumption in the analysis of hydrogen consumption of FCEV in their long-term operation is presented below.

3. Results and Discussion

Sample operating data of seven hydrogen-powered cars were used to determine the energy footprint.

The calculation results are presented in

Table 1. Operational data of analyzed FCEVs.

	Vehicle No.	Operation Period		Vehicle Mileage		Distance	Refueling No.
		Beginning	End	Beginning	End
				km	km	km
1	1151077	25.10.2019	14.01.2022	370	51,943	51,573	197
2	1254191	24.07.2021	20.05.2022	20	9952	9932	40
3	1316958	20.12.2021	31.01.2022	26,894	28,762	1868	8
4	1195603	15.08.2020	26.08.2021	33,995	44,797	10,802	65
5	1242674	20.04.2021	27.04.2021	2.409	4234	1825	5
6	1349603	22.04.2022	08.10.2022	24	5658	5634	12
7	1394273	03.05.2022	10.10.2022	26,359	35,905	9546	31

and

Table 2. Coefficients of model (4) and operational environment data of the analyzed cars.

	Vehicle 1151077	Vehicle 1254191	Vehicle 1316958	Vehicle 1195603	Vehicle 1242674	Vehicle 1349603	Vehicle 1394273
Coefficient c	0.020574	0.018053	0.005089	0.026619	0.006109	0.007836	0.011271
Coefficient a	−0.056978	−0.057913	0.072905	−0.048746	0.015091	−0.008227	−0.006655
R-Square	0.998687	0.993882	0.985653	0.997509	0.999922	0.999762	0.999547
Vehicle operation environment
Motorway, %	30	38	14	16	33	48	32
City, %	26	31	39	24	33	52	33
Country roads, %	34	31	41	60	34	0	35
Refueling no.	197	40	8	65	5	12	31

.

Mileages range from tens to thousands of kilometers (each car separately). Also, the refueling numbers are relatively large.

A mathematical model (4) was determined separately for each car. To know the mathematical model, it was necessary to determine the values of both coefficients, i.e., “c” and “a”. A full analysis of the statistical adequacy of model (4) was then performed (with respect to the operational data of each FCEV). Some of the obtained data are presented in Table 2.

The R-square (correlation coefficient) of the adequacy of model (4) is in each case unexpectedly high and is greater than 0.98 (Table 2). An illustration of the model’s adequacy to the results from natural exploitation is provided. Since the largest number of refueling was recorded in the case of vehicle number 1151077 (Table 1), data from this particular vehicle were selected to illustrate the method of determining the energy footprint of a car.

The data presented in Figure 2 show that there are differences between the values calculated using model (4) (CFC) and the values from natural exploitation (CFCm). It is clear at first glance that these differences are not constant and are not significant anywhere. Please compare the values of percentage deviations (%). Outside the initial period of operation, the deviations of the calculated and measured heights are within the range of ±2.5%. As mileage increases, the differences decrease (this happens often).

It should be remembered that the data do not come from specialized tests, but are data recorded by vehicle users. It is also worth paying attention to the fact that the approximating curve (CFC) runs through the points illustrating the measurement (CFCm) in a quite characteristic way, i.e., sometimes their series is above and then below the curve, then again it is above the curve, etc. This may indicate that that there are additional factors affecting hydrogen consumption, such as ambient temperature. Unfortunately, the database contains the dates when the measurement was made, but no weather data, e.g., temperature. There is also no geographical information where the vehicle was used, which makes it impossible to correlate various databases and conduct a broader study of the material.

The vehicle’s energy footprint includes Equations (4)–(6) if the values of the “c” and “a” coefficients are known (as here in Table 2). An example energy footprint of FCEV No. 1151077 is shown here in Figure 3.

This is a typical image of a vehicle’s energy footprint. In the case of this particular vehicle (1151077), measurement data (CFC) was recorded from the beginning of its operation. However, operational data are not always recorded from the time the vehicle is placed in service.

It is commonly believed that the intensity of energy consumption increases with mileage, but this is not all case—see Figure 3. As already mentioned, multiplying SCFC by 100 gives 100SCFC, which corresponds to AFE. Figure 4 shows a comparison of these values.

Here, 100SCFC appears to be a metric that reflects operational reality much better than AFE.

The energy footprint can be determined for virtually any vehicle, provided that its operational data are known. As a side note, it is worth mentioning that before each new vehicle is put into operation, many months of operational tests of many of its prototypes are carried out, most often in very diverse conditions. Therefore, there is no shortage of operational data, so it seems that they should only be developed more comprehensively to determine its energy footprint.

Figure 5 shows the curves of the cumulative fuel consumption of the vehicles listed in Table 1 and Table 2.

The highest cumulative hydrogen consumption was recorded in the case of FCEV 1195603 operation. This is because 60% of the mileage of this vehicle was on rural roads (Table 2). Relatively high cumulative fuel consumption was also recorded in the case of vehicle 1316958. Table 2 shows that 40% of the mileage of this vehicle was on rural roads. Therefore, the common opinion is confirmed that the conditions and environment of vehicle use are important for FE (it seems that without knowing the vehicle’s energy footprint, it would be more difficult to detect it). In addition, it can be said that hydrogen-powered vehicles are relatively sensitive to operating conditions—the range of CFC curves is relatively large. For now, there is too little data to link hydrogen CFC with operating conditions. However, as data becomes available, it seems likely that such relationships will be created (this was performed for battery-powered electric vehicles in preparation for publication).

Interesting information is provided by the analysis of the percentage deviations of the measured values of CFC and the calculated values (CFCc)—see Figure 6. The greatest deviations of the calculated values from the measured ones occur at the beginning of the operation when the cumulated fuel consumption is relatively small. If the vehicle mileage increases, these deviations decrease. Such a regularity was also noticed in the case of the analysis of the consumption of other fuels used in transport. Therefore, it seems that also in the case of pure hydrogen supply, it is possible to use the theory of CFC for long-term prognoses.

Figure 7 shows the specific cumulative hydrogen consumption curves (SCFC).

SCFCs are similar in many cases (although there are different trends), but there are also curves that are clearly different from the others, e.g., SCFC curve 1195603 (Figure 7). Explaining this phenomenon is not the subject of this publication (but please pay attention to the data presented in Table 1). However, it is worth noting here that there is already a tool (CFC theory) that can be used in further analyses.

If data from natural use are analyzed, it is usually difficult to find such data that would facilitate estimating fuel consumption, e.g., for hydrogen, corresponding to a specific mileage, e.g., 20,000 km. For example, Table 1 shows that such a mileage occurs only in the case of one vehicle (1151077). The data relating to the operation of the remaining vehicles show that either such mileage was not achieved (vehicles 1254191, 1242674 and 13496030) or the initial mileage was higher than the assumed 20,000 km (vehicles 1316958, 1195603 and 1394273). In such a situation, without knowing the trace energy footprint of each vehicle, no further conclusions can be drawn. The situation changes when the energy footprint of the vehicle is known, and specifically when the coefficients a and c are known, i.e., when the specific form of Equation (4) is known for each vehicle (Table 2), CFC curves can be developed (Figure 5). Since the presented curves concern vehicles of one brand and type, further analyses can be presented for an ’average’ vehicle. The results of such analyses are presented in Figure 8.

The figure shows the average hydrogen CFC curve—AV (average). Since the CFC values of hydrogen are known for any course, further statistical characteristics could be determined—including standard deviations (SD). Therefore, Figure 8 shows the range of deviations from the mean, i.e., mean ± standard deviation (AV ± SD). As can be seen, this range is relatively wide (a very small statistical sample of only seven cars was analyzed). Knowing both the mean and the standard deviation value leads to further analyses. Figure 8 shows, for example, that a vehicle of the analyzed type will consume 2000 kg of hydrogen after approximately 210,000 km. However, with the same probability (in this case 95%), it can be expected that the consumption at 210,000 km will amount to 2000 ± 350 kg of hydrogen (range A in Figure 8). On the other hand, it can be expected (also with a probability of 95%) that the consumption of 2000 kg of hydrogen may occur both at a mileage of 175,000 km and at a mileage of 265,000 km (range B in Figure 8).

Figure 8 shows that the range characterizing deviations from the average increases with the increase in vehicle mileage. It is interesting, however, that if we use a well-known statistical measure, which is the coefficient of variation (CV = SD/AV), then, as can be seen in Figure 8, this coefficient is almost unchanged throughout the predicted mileage of this brand and type of car.

In the case analyzed (in this publication), seven CFC values correspond to each mileage value. One can consider what the statistical distribution of these seven values is. Due to the small sample size (seven elements), this distribution can only be described indirectly. Using simplified relationships, it is possible to determine the value of kurtosis and skewness of such a distribution. Figure 8 shows how the values of kurtosis and skewness change as a function of vehicle mileage. It can be seen that at the beginning of operation, both coefficients have negative values—the distribution is with the maximum shifted towards smaller values like the average and is relatively flat. After reaching approximately 170,000 km of mileage, the distribution of hydrogen CFC values becomes similar to the normal distribution. In further operation, the maximum value of the distribution becomes larger than the average, and the distribution becomes sharper. If the database were broader (e.g., the vehicle fleet consisted of over 30 vehicles), hydrogen consumption could be analyzed more precisely and optimization aimed at reducing this consumption would be possible; thus, reducing energy consumption (obtaining hydrogen is very energy-intensive).

At the beginning of this article, it was mentioned that it is possible to determine the CFC of individual vehicles as well as to determine the total (SUM) of CFC of all tested vehicles (as a fleet for example), of course, as a Function (7) of their mileage.

$${SUM CFC=SUM}_k\left(t_d\right)={\sum }_{i=1}^k{CFC}_i\left(t_d\right)$$

(7)

k—number of vehicles in the fleet.

The required calculations were performed and their results are presented in Figure 9.

As it can be seen, regardless of the fact that one of the curves (car 1195603) differs significantly from the others, the SUM CFC curve is rather similar to the CFC curves. The SUM CFC presentation is intended to show the possibility of estimating the necessary amount of fuel, among others, for the use of the fleet within the assumed mileage.

This publication does not present a broader statistical evaluation—unlike in the paper [93]. Each vehicle (even the same brand and type as here) is, of course, operated differently. This is reflected in the CFC and will undoubtedly have an impact on the forecast values. However, this does not change the fact that the CFC theory may be useful in this type of analysis, and by having a more extensive database and supplementing the arguments with statistical analyses, one can expect more accurate forecasts.

4. Conclusions

Various activities are being undertaken more and more frequently and intensively to reduce CO₂ emissions, the excessive concentration of which in the atmosphere is a global problem. Such activities also apply to transport. It is proposed, among other aspects, that the use of e-fuels, i.e., fuels derived exclusively from renewable sources, should be considered. An example of an e-fuel may be ’green’ hydrogen.

The multi-faceted literature study presented in this work shows that there are quite large discrepancies in the assessment of the suitability of hydrogen as an e-fuel in the future. It is rather expected that wider implementation of hydrogen will require additional political decisions, as it does not result directly from the economics of the process.

The analysis of the literature shows a fundamental conclusion regarding further work on the implementation of hydrogen as a fuel in transport, i.e., there are no reliable assessments of the demand for hydrogen in the long-term natural operation of both individual vehicles of a given brand and type, as well as vehicle fleets. This lack of needs assessment results directly from the lack of methods for carrying out such an assessment.

When implementing new types of e-fuels, it is necessary to precisely estimate the demand for them in order to meet the demand for them in the natural operation of vehicles. This estimation requires knowledge of the energy footprint of the vehicle(s). The article presents a method of creating such a footprint for hydrogen-powered cars.

The method of creating an energy footprint for a specific vehicle (and a fleet of such vehicles) is illustrated by the results of the analysis of operational data of seven hydrogen-powered cars. The data used are publicly available. The presented method can therefore be independently (and easily) verified—which was one of the assumptions of this publication.

It has been shown that it is possible to obtain information that has not been possible so far. Without knowing the vehicle’s energy footprint, it is impossible to assess the actual fuel consumption values or the predictive total fuel consumption of the fleet over the long period of its operation.

The use of the vehicle energy footprint assessment method organizes activities related to the assessment of fuel consumption in long-term vehicle operation. It also allows one to select anomalies, including vehicles whose fuel consumption is clearly different from others. Therefore, it is estimated that this method can be used to assess the impact of operating conditions on fuel consumption in the long term.

It is also important to mention that by having a theory (CFC) leading to determining the energy footprint of a vehicle of a given brand and type, it is possible to conduct analyses and obtain information, e.g., about confidence intervals for averages or changes in the distribution of values within these intervals as a function of mileage. This is fundamental for assessing energy demand, especially for vehicle fleets.

It seems that a useful tool has been developed and presented, which may have wide applications in the future—especially in LCA analyses. Of course, this requires a much larger database, which would enable (very important) broader statistical analysis.

More and more vehicle manufacturers are introducing systems for remote monitoring of individual vehicles during their long-term operation. Therefore, it seems advisable that the collected data be analyzed broadly and in many aspects, and not only by vehicle manufacturers. This would allow not only to monitor the current demand, e.g., for hydrogen as a fuel, but also to forecast this demand quite accurately—which, of course, is very important for many different reasons. A tool for such an analysis may include, among others, the vehicle’s energy footprint, resulting from the theory of cumulative fuel consumption.