Exploring Future Promising Technologies in Hydrogen Fuel Cell Transportation

The purpose of this research was to derive promising technologies for the transport of hydrogen fuel cells, thereby supporting the development of research and development policy and presenting directions for investment. We also provide researchers with information about technology that will lead the technology field in the future. Hydrogen energy, as the core of carbon neutral and green energy, is a major issue in changing the future industrial structure and national competitive advantage. In this study, we derived promising technology at the core of future hydrogen fuel cell transportation using the published US patent and paper databases (DB). We first performed text mining and data preprocessing and then discovered promising technologies through generative topographic mapping analysis. We analyzed both the patent DB and treatise DB in parallel and compared the results. As a result, two promising technologies were derived from the patent DB analysis, and five were derived from the paper DB analysis.

Developing a Hydrogen Fuel Cell Vehicle (HFCV) Energy Consumption Model for Transportation Applications

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.

Forecasting the Hydrogen Demand in China: A System Dynamics Approach

Many countries, including China, have implemented supporting policies to promote the commercialized application of green hydrogen and hydrogen fuel cells. In this study, a system dynamics (SD) model is proposed to study the evolution of hydrogen demand in China from the petroleum refining industry, the synthetic ammonia industry, and the vehicle market. In the model, the impact from the macro-environment, hydrogen fuel supply, and construction of hydrogen facilities is considered to combine in incentives for supporting policies. To further formulate the competitive relationship in the vehicle market, the Lotka–Volterra (LV) approach is adopted. The model is verified using published data from 2003 to 2017. The model is also used to forecast China’s hydrogen demand up to the year of 2030 under three different scenarios. Finally, some forward-looking guidance is provided to policy makers according to the forecasting results.

A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network

The remaining useful life (RUL) prediction for hydrogen fuel cells is an important part of its prognostics and health management (PHM). Artificial neural networks (ANNs) are proven to be very effective in RUL prediction, as they do not need to understand the failure mechanisms behind hydrogen fuel cells. A novel RUL prediction method for hydrogen fuel cells based on the gated recurrent unit ANN is proposed in this paper. Firstly, the data were preprocessed to remove outliers and noises. Secondly, the performance of different neural networks is compared, including the back propagation neural network (BPNN), the long short-term memory (LSTM) network and the gated recurrent unit (GRU) network. According to our proposed method based on GRU, the root mean square error was 0.0026, the mean absolute percentage error was 0.0038 and the coefficient of determination was 0.9891 for the data from the challenge datasets provided by FCLAB Research Federation, when the prediction starting point was 650 h. Compared with the other RUL prediction methods based on the BPNN and the LSTM, our prediction method is better in both prediction accuracy and convergence rate.

Use of Hydrogen as Fuel: A Trend of the 21st Century

The unbridled use of fossil fuels is a serious problem that has become increasingly evident over the years. As such fuels contribute considerably to environmental pollution, there is a need to find new, sustainable sources of energy with low emissions of greenhouse gases. Climate change poses a substantial challenge for the scientific community. Thus, the use of renewable energy through technologies that offer maximum efficiency with minimal pollution and carbon emissions has become a major goal. Technology related to the use of hydrogen as a fuel is one of the most promising solutions for future systems of clean energy. The aim of the present review was to provide an overview of elements related to the potential use of hydrogen as an alternative energy source, considering its specific chemical and physical characteristics as well as prospects for an increase in the participation of hydrogen fuel in the world energy matrix.

Methane hydrogen laminar burning velocity blending laws in horizontal open-ended flame tube rig

Different fuel properties and chemical kinetics of two different fuels would make it challenging to predict the combustion parameters of a binary fuel. Understanding the effect of blending methane and hydrogen gas is the main focus of this paper. Utilizing a horizontal tube combustion rig, methane-hydrogen fuel blends were created using blending laws from past literature, over a range of equivalence ratios from 0.6 – 1.2 were studied, while keeping one combustion parameter constant, the theoretical laminar burning velocity. The selected theoretical laminar burning velocity for all the mixtures tested were kept constant at 0.6 ms−1. Different factors affected the flame propagation across the tube, including acoustic pressure oscillations, heat loss from the rig, and obvious difference in hydrogen percentage in the fuel blends. The average experimental laminar burning velocity of all the flames was 0.368 ms−1, compared to the expected value of 0.6 ms−1. In an attempt to keep the theoretical laminar burning velocity constant for different mixtures, it was discovered that this did not promise the same flame propagation behaviour for the tested mixtures. Further experimentation and analysis are required in order to better understand the underlying interaction of the fuel blends.

Pilot autonomous hybrid hydrogen refueling station utilizing a metal hydride compressor covering local transportation needs

The need for decreasing carbon emissions in the transportation sector in order to meet the targets of the European Union by 2030, inevitably leads to the large scale adoption of cleaner alternatives. Hydrogen fueled vehicles could possibly provide one such alternative, if we could assume that the necessary infrastructure would be widely available throughout Europe. Already, the European Union has committed to the construction of a significant number of Hydrogen Refueling Stations (HRS) by year 2025 and in view of that, there is a need of developing suitable configurations for the production, compression, storage and dispensing of green hydrogen to hydrogen fueled vehicles. This work presents an autonomous hybrid system which produces green hydrogen by PV- powered water electrolysis (PEM), which is subsequently compressed by a novel metal hydride hydrogen compressor to pressures up to 200 bar. This pilot HRS will meet the daily demand of 2 scooters and a golf cart which have been transformed, in order for their electric motor to be powered by a hydrogen fuel cell instead of a battery. An important element of the work which is presented, revolves around the integration of the metal hydride compressor with the rest of the system, and how this integration won’t hinder its functionality. The complete system design and layout is presented, while the results from the system operation could give a good idea regarding the optimal system sizing for similar large scale applications.

Current Status and Future Development of Fuel Cell Ships in China

The high quality development of fuel cell ship industry is of great significance for China to achieve carbon dioxide peaking and carbon neutrality. The research of fuel cell ships in China is still in its early stage and faces many challenges to achieve industrialization. In this paper, the types and characteristics of fuel cells are introduced, and the fuel cell types suitable for marine applications are identified. Then, the research status of fuel cell ship projects in China and abroad is introduced. By comparing fuel cell ship projects, the gap between domestic and foreign fuel cell ship projects is discussed. Finally, in view of the existing problems of fuel cell ships in China, some suggestions for the development of marine hydrogen fuel cells in China are put forward, and the future development of marine hydrogen fuel cell technology in China is prospected.