Abstract
This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model.
Highlights
In recent years, more than ever, our world is threatened with a strong dependence on fossil fuels, especially oil and coal
Hydrogen gas is employed as an ideal fuel in fuel cells due to its high reactivity, abundance, and environmental pollution
The main purpose here is to optimal designing of a proton exchange membrane fuel cell (PEMFC) model with high precision
Summary
More than ever, our world is threatened with a strong dependence on fossil fuels, especially oil and coal. According to NASA’s Goddard Space Flight Center, the amount of carbon dioxide in the air has been at its highest value for the past 7000 years which is 1 ppm Alongside these problems, glacial melt and rising sea levels are other climate change problems (Shaw et al, 2018). Among different types of renewable and clean energies, hydrogen is one of the new popular sources that have unique advantages toward the others, such as its very low pollution, its reversibility during the production cycle, and its low effect on the greenhouse effect. PEMFCs are the most efficient energy generation among different types of fuel cells. They operate fast with no contamination and low operational temperature in the range 60 ◦C–80 ◦C (Zhou and Dhupia, 2020). As aforementioned, the proposed optimal network is employed for nonlinear modeling of a PEMFC system
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