Machine learning aided methods for reducing the dimensionality of the comprehensive energy economy optimization of the fuel cell powertrains

2021 ◽  
pp. 129250
Author(s):  
Xingyu Zhou ◽  
Feng chun Sun ◽  
Chao Sun
Keyword(s):  
Machine Learning ◽  
Fuel Cell ◽  
Energy Economy

scholarly journals The End of the M.E.?

2005 ◽  
Vol 127 (05) ◽  
pp. 26-29 ◽  
Author(s):  
Peter Huber ◽  
Mark P. Mills
Keyword(s):  
Fuel Cell ◽  
Mechanical Engineering ◽  
Combustion Engine ◽  
Electrical Power ◽  
Single Step ◽  
Second Century ◽  
Basic Operation ◽  
The Road ◽  
Energy Economy ◽  
On The Road

This article highlights that mechanical engineers control most of the rest of our energy economy. The engineering focus will shift inexorably toward finding the most efficient means of generating electricity on-board. Trains and monster trucks both use big diesel generators. Hybrid cars on the road today burn gasoline, but it is the fuel cell that attracts the most attention from visionaries and critics of the internal combustion engine. Remarkably elegant in its basic operation, the fuel cell transforms fuel into electricity in a single step, completely bypassing the furnace, turbine, and generator. In this scenario, mechanical engineering ultimately surrenders its last major under-the-hood citadel to chemical engineers. One might say that the age of mechanical engineering was launched by James Watt's steam engine in 1763, and propelled through its second century by Nikolaus Otto’s 1876 invention of the spark-ignited petroleum engine. We are now at the dawn of the age of electrical engineering, not because we recently learned how to generate light-speed electrical power, but because we have now finally learned how to control it.

Sustainable hydrogen energy

2007 ◽  
Author(s):  
Peter P. Edwards ◽  
Vladimir L. Kuznetsov
Keyword(s):  
Fuel Cells ◽  
Fuel Cell ◽  
Energy Supply ◽  
Fossil Fuel ◽  
Low Cost ◽  
Hydrogen Energy ◽  
The Future ◽  
Energy Economy ◽  
Fossil Fuel Energy ◽  
Carbon Based

Hydrogen is the simplest and most abundant chemical element in our universe— it is the power source that fuels the Sun and its oxide forms the oceans that cover three quarters of our planet. This ubiquitous element could be part of our urgent quest for a cleaner, greener future. Hydrogen, in association with fuel cells, is widely considered to be pivotal to our world’s energy requirements for the twenty-first century and it could potentially redefine the future global energy economy by replacing a carbon-based fossil fuel energy economy. The principal drivers behind the sustainable hydrogen energy vision are therefore: • the urgent need for a reduction in global carbon dioxide emissions; • the improvement of urban (local) air quality; • the abiding concerns about the long-term viability of fossil fuel resources and the security of our energy supply; • the creation of a new industrial and technological energy base—a base for innovation in the science and technology of a hydrogen/fuel cell energy landscape. The ultimate realization of a hydrogen-based economy could confer enormous environmental and economic benefits, together with enhanced security of energy supply. However, the transition from a carbon-based(fossil fuel) energy system to a hydrogen-based economy involves significant scientific, technological, and socio-economic barriers. These include: • low-carbon hydrogen production from clean or renewable sources; • low-cost hydrogen storage; • low-cost fuel cells; • large-scale supporting infrastructure, and • perceived safety problems. In the present chapter we outline the basis of the growing worldwide interest in hydrogen energy and examine some of the important issues relating to the future development of hydrogen as an energy vector. As a ‘snapshot’ of international activity, we note, for example, that Japan regards the development and dissemination of fuel cells and hydrogen technologies as essential: the Ministry of Economy and Industry (METI) has set numerical targets of 5 million fuel cell vehicles and10 million kW for the total power generation by stationary fuel cells by 2020. To meet these targets, METI has allocated an annual budget of some £150 million over four years.

A fast Fuel Cell Parametric Identification approach based on Machine Learning Inverse Models

Energy ◽  
2021 ◽  
pp. 122140
Author(s):  
Antonio Guarino ◽  
Riccardo Trinchero ◽  
Flavio Canavero ◽  
Giovanni Spagnuolo
Keyword(s):  
Machine Learning ◽  
Fuel Cell ◽  
Parametric Identification ◽  
Inverse Models ◽  
Identification Approach

scholarly journals The 2‐Propanol Fuel Cell: A Review from the Perspective of a Hydrogen Energy Economy

2021 ◽  
Author(s):  
Matthew Brodt ◽  
Karsten Müller ◽  
Jochen Kerres ◽  
Ioannis Katsounaros ◽  
Karl Mayrhofer ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Hydrogen Energy ◽  
Energy Economy

scholarly journals Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2068
Author(s):  
Mohamed Derbeli ◽  
Cristian Napole ◽  
Oscar Barambones
Keyword(s):  
Machine Learning ◽  
Fuel Cell ◽  
Pem Fuel Cell ◽  
Cell System ◽  
Operating Conditions ◽  
Effect Of Temperature ◽  
Fuel Cell System ◽  
Wide Range ◽  
Temperature And Humidity ◽  
Predicted Model

In recent years, machine learning (ML) has received growing attention and it has been used in a wide range of applications. However, the ML application in renewable energies systems such as fuel cells is still limited. In this paper, a prognostic framework based on artificial neural network (ANN) is designed to predict the performance of proton exchange membrane (PEM) fuel cell system, aiming to investigate the effect of temperature and humidity on the stack characteristics and on tracking control improvements. A large part of the experimental database for various operating conditions has been used in the training operation to achieve an accurate model. Extensive tests with various ANN parameters such as number of neurons, number of hidden layers, selection of training dataset, etc., are performed to obtain the best fit in terms of prediction accuracy. The effect of temperature and humidity based on the predicted model are investigated and compared to the ones obtained from real-time experiments. The control design based on the predicted model is performed to keep the stack operating point at an adequate power stage with high-performance tracking. Experimental results have demonstrated the effectiveness of the proposed model for performance improvements of PEM fuel cell system.

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