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Due to the large quantities of carbon emissions generated by the transportation sector, cleaner automotive technologies are needed aiming at a green energy transition. In this scenario, hydrogen is pointed out as a promising fuel that can be employed as the fuel of either a fuel cell or an internal combustion engine vehicle. Therefore, in this work, we propose the design and modeling of a fuel cell versus an internal combustion engine passenger car for a driving cycle. The simulation was carried out using the quasistatic simulation toolbox tool in Simulink considering the main powertrain components for each vehicle. Furthermore, a brief analysis of the carbon emissions associated with the hydrogen production method is addressed to assess the clean potential of hydrogen-powered vehicles compared to conventional fossil fuel-fueled cars. The resulting analysis has shown that the hydrogen fuel cell vehicle is almost twice as efficient compared to internal combustion engines, resulting in a lower fuel consumption of 1.05 kg-H2/100 km in the WLTP driving cycle for the fuel cell vehicle, while the combustion vehicle consumed about 1.79 kg-H2/100 km. Regarding using different hydrogen colors to fuel the vehicle, hydrogen-powered vehicles fueled with blue and grey hydrogen presented higher carbon emissions compared to petrol-powered vehicles reaching up to 2–3 times higher in the case of grey hydrogen. Thus, green hydrogen is needed as fuel to keep carbon emissions lower than conventional petrol-powered vehicles.

Facing global warming and recent bans on the use of diesel in vehicles, there is a growing need to develop vehicles powered by renewable energy sources to mitigate greenhouse gas and pollutant emissions. Among the various forms of non-fossil energy for vehicles, hydrogen fuel is emerging as a promising way to combat global warming. To date, most studies on vehicle carbon emissions have focused on diesel and electric vehicles (EVs). Emission assessment methodologies are usually developed for fast-moving consumer goods (FMCG) which are non-durable household goods such as packaged foods, beverages, and toiletries instead of vehicle products. There is an increase in the number of articles addressing the product carbon footprint (PCF) of hydrogen fuel cell vehicles in the recent years, while relatively little research focuses on both vehicle PCF and fuel cycle. Zero-emission vehicles initiative has also brought the importance of investigating the emission throughout the fuel cycle of hydrogen fuel cell and its environmental impact. To address these gaps, this study uses the life-cycle assessment (LCA) process of GREET (greenhouse gases, regulated emissions, and energy use in transportation) to compare the PCF of an EV (Tesla Model 3) and a hydrogen fuel cell car (Toyota MIRAI). According to the GREET results, the fuel cycle contributes significantly to the PCF of both vehicles. The findings also reveal the need for greater transparency in the disclosure of relevant information on the PCF methodology adopted by vehicle manufacturers to enable comparison of their vehicles’ emissions. Future work will include examining the best practices of PCF reporting for vehicles powered by renewable energy sources as well as examining the carbon footprints of hydrogen production technologies based on different methodologies.

This paper provides an in-depth review of the current state and future potential of hydrogen fuel cell vehicles (HFCVs). The urgency for more eco-friendly and efficient alternatives to fossil-fuel-powered vehicles underlines the necessity of HFCVs, which utilize hydrogen gas to power an onboard electric motor, producing only water vapor and heat. Despite their impressive energy efficiency ratio (EER), higher power-to-weight ratio, and substantial emissions reduction potential, the widespread implementation of HFCVs is presently hindered by several technical and infrastructural challenges. These include high manufacturing costs, the relatively low energy density of hydrogen, safety concerns, fuel cell durability issues, insufficient hydrogen refueling infrastructure, and the complexities of hydrogen storage and transportation. Nevertheless, technological advancements and potential policy interventions offer promising prospects for HFCVs, suggesting they could become a vital component of sustainable transportation in the future.

This paper presents a simple hydrogen fuel cell vehicle (HFCV) energy consumption model. Simple fuel/energy consumption models have been developed and employed to estimate the energy and environmental impacts of various transportation projects for internal combustion engine vehicles (ICEVs), battery electric vehicles (BEVs), and hybrid electric vehicles (HEVs). However, there are few published results on HFCV energy models that can be simply implemented in transportation applications. The proposed HFCV energy model computes instantaneous energy consumption utilizing instantaneous vehicle speed, acceleration, and roadway grade as input variables. The mode accurately estimates energy consumption, generating errors of 0.86% and 2.17% relative to laboratory data for the fuel cell estimation and the total energy estimation, respectively. Furthermore, this work validated the proposed model against independent data and found that the new model accurately estimated the energy consumption, producing an error of 1.9% and 1.0% relative to empirical data for the fuel cell and the total energy estimation, respectively. The results demonstrate that transportation engineers, policy makers, automakers, and environmental engineers can use the proposed model to evaluate the energy consumption effects of transportation projects and connected and automated vehicle (CAV) transportation applications within microscopic traffic simulation models.

The temperature rises hydrogen tanks during the fast-filling process could threaten the safety of the hydrogen fuel cell vehicle. In this paper, a 2D axisymmetric model of a type III hydrogen for the bus was built to investigate the temperature evolution during the fast-filling process. A test rig was carried out to validate the numerical model with air. It was found significant temperature rise occurred during the filling process, despite the temperature of the filling air being cooled down due to the throttling effect. After verification, the 2D model of the hydrogen tank was employed to study the temperature distribution and evolution of hydrogen during the fast-filling process. Thermal stratification was observed along the axial direction of the tank. Then, the effects of filling parameters were examined, and a formula was fitted to predict the final temperature based on the simulated results. At last, an effort was paid on trying the improve the temperature distribution by increasing the injector length of the hydrogen tank. The results showed the maximal temperature and mass averaged temperature decreased by 2 K and 3.4 K with the length of the injector increased from 50 mm to 250 mm.

Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the energy modeling of HFCVs. To address this gap, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. Our HFCV energy model leverages real-time vehicle speed, acceleration, and roadway grade data to determine instantaneous power exertion for the computation of hydrogen fuel consumption, battery energy usage, and overall energy consumption. The results suggest that the model’s forecasts align well with real-world data, demonstrating average error rates of 0.0% and −0.1% for fuel cell energy and total energy consumption across all four cycles. However, it is observed that the error rate for the UDDS drive cycle can be as high as 13.1%. Moreover, the study confirms the reliability of the proposed model through validation with independent data. The findings indicate that the model precisely predicts energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Furthermore, the model is compared to FASTSim, which was developed by the National Renewable Energy Laboratory (NREL), and the difference between the two models is found to be around 2.5%. Additionally, instantaneous battery state of charge (SOC) predictions from the model closely match observed instantaneous SOC measurements, highlighting the model’s effectiveness in estimating real-time changes in the battery SOC. The study investigates the energy impact of various intersection controls to assess the applicability of the proposed energy model. The proposed HFCV energy model offers a practical, versatile alternative, leveraging simplicity without compromising accuracy. Its simplified structure reduces computational requirements, making it ideal for real-time applications, smartphone apps, in-vehicle systems, and transportation simulation tools, while maintaining accuracy and addressing limitations of more complex models.

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

The hydrogen storage tank is a key parameter of the hydrogen storage system in hydrogen fuel cell vehicles (HFCVs), as its safety determines the commercialization of HFCVs. Compared with other types, the type IV hydrogen storage tank which consists of a polymer liner has the advantages of low cost, lightweight, and low storage energy consumption, but meanwhile, higher hydrogen permeability. A detailed review of the existing research on hydrogen permeability of the liner material of type IV hydrogen storage tanks can improve the understanding of the hydrogen permeation mechanism and provide references for following-up researchers and research on the safety of HFCVs. The process of hydrogen permeation and test methods are firstly discussed in detail. This paper then analyzes the factors that affect the process of hydrogen permeation and the barrier mechanism of the liner material and summarizes the prediction models of gas permeation. In addition to the above analysis and comments, future research on the permeability of the liner material of the type IV hydrogen storage tank is prospected.

About 95% of current hydrogen production uses technologies involving primary fossil resources. A minor part is synthesized by low-carbon and close-to-zero-carbon-footprint methods using RESs. The significant expansion of low-carbon hydrogen energy is considered to be a part of the “green transition” policies taking over in technologically leading countries. Projects of hydrogen synthesis from natural gas with carbon capture for subsequent export to European and Asian regions poor in natural resources are considered promising by fossil-rich countries. Quality changes in natural resource use and gas grids will include (1) previously developed scientific groundwork and production facilities for hydrogen energy to stimulate the use of existing natural gas grids for hydrogen energy transport projects; (2) existing infrastructure for gas filling stations in China and Russia to allow the expansion of hydrogen-fuel-cell vehicles (HFCVs) using typical “mini-plant” projects of hydrogen synthesis using methane conversion technology; (3) feasibility testing for different hydrogen synthesis plants at medium and large scales using fossil resources (primarily natural gas), water and atomic energy. The results of this study will help focus on the primary tasks for quality changes in natural resource and gas grid use. Investments made and planned in hydrogen energy are assessed.

The development of new energy vehicles, particularly electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs), represents a strategic initiative to address climate change and foster sustainable development. Integrating PV with hydrogen production into hybrid electricity-hydrogen energy stations enhances land and energy efficiency but introduces scheduling challenges due to uncertainties. A multi-time scale scheduling framework, which includes day-ahead and intraday optimization, is established using fuzzy chance-constrained programming to minimize costs while considering the uncertainties of PV generation and charging/refueling demand. Correspondingly, trapezoidal membership function and triangular membership function are used for the fuzzy quantification of day-ahead and intraday predictions of photovoltaic power generation and load demands. The system achieves 29.37% lower carbon emissions and 17.73% reduced annualized costs compared to day-ahead-only scheduling. This is enabled by real-time tracking of PV/load fluctuations and optimized electrolyzer/fuel cell operations, maximizing renewable energy utilization. The proposed multi-time scale framework dynamically addresses short-term fluctuations in PV generation and load demand induced by weather variability and temporal dynamics. By characterizing PV/load uncertainties through fuzzy methods, it enables formulation of chance-constrained programming models for operational risk quantification. The confidence level – reflecting decision-makers’ reliability expectations – progressively increases with refined temporal resolution, balancing economic efficiency and operational reliability.