Abstract
Parameter identification of the proton exchange membrane fuel cell (PEMFC) is a good way of increasing their efficiency in the next designs. In this study, an optimized improved Elman neural network based on a new hybrid optimization algorithm is proposed for this purpose. The proposed algorithm is a hybrid algorithm based on a combination of two newly algorithms, the world cup optimization (WCO) and the fluid Search Optimization (FSO) algorithms. The proposed method is applied to improve the method efficiency for estimating the PEMFC model parameters. The method is then validated by four different operational conditions. The optimization algorithm efficiency is also analyzed by comparison with some popular algorithms. Simulation results showed that using the designed method gives higher accuracy forecast for the PEMFC model parameters.
Highlights
Energy has been turned into a driving force for comprehensive economic development in all countries which makes the use of available energy sources as a major factor in the economic development of post-human societies (Mirzapour et al, 2019; Ahadi et al, 2015; Aghajani and Ghadimi, 2018)
Fuel cells are a kind of renewable energy sources that uses electrochemical reactions for energy reduction
The purpose of this paper is to investigate the proton exchange membrane fuel cell (PEMFC) model to improve its efficiency
Summary
Energy has been turned into a driving force for comprehensive economic development in all countries which makes the use of available energy sources as a major factor in the economic development of post-human societies (Mirzapour et al, 2019; Ahadi et al, 2015; Aghajani and Ghadimi, 2018). The usage of the fuel cell (FC) has been exponentially increasing. A fuel cell is a power generation component which converts the chemical power directly into the electrical power and the heating power (Saeedi et al, 2019; Abedinia et al, 2019). This type of source energy is widely used as the promising portable plant especially in portable and motionless applications like electric vehicles and UAVs (Liu et al, 2017; Ijaodola et al, 2019). Polymer electrolyte membrane (PEM) has become a promising model against the other types of fuel cells based on its characteristics
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