Abstract
In this paper, a new approach has been introduced for optimal parameter estimation of a proton exchange membrane fuel cell (PEMFC) model. The main purpose is to minimize the total error between the empirical data and the proposed method by optimal parameter selection of the model. The methodology is based on using a newly introduced developed version of the Coyote Optimization Algorithm (DCOA) for determining the value of the unknown parameters in the model. Two different PEMFC models including 2 kW Nexa FC and 6kW NedSstack PS6 FC are adopted for validation and the results are compared with the empirical data and some well-known methods including conventional COA, Seagull Optimization Algorithm, and (N + λ) - ES algorithm to show the proposed method’s superiority toward the literature methods. The final results declared a satisfying agreement between the proposed DCOA and the empirical data. The results also declared the excellence of the presented method toward the other compared methods.
Highlights
One of the most important challenges facing humankind is to find new and efficient ways of converting fuels into usable energy (Zhang et al, 2016)
The results showed that catalyst loading distribution has a principal impact on the proton exchange membrane fuel cell (PEMFC) efficiency such that based on their founding, it can increase the performance by about 14%
The results were investigated based on two empirical examples and the achievements were compared with several algorithms reported from the literature to declare the algorithm’s excellence toward the compared methods
Summary
One of the most important challenges facing humankind is to find new and efficient ways of converting fuels into usable energy (Zhang et al, 2016). The computational model for the PEMFC is a mathematical representation of the physical phenomena as well as the electrochemical processes governing the performance of the fuel cell. The computational models of the polymer membrane fuel cell are used for two purposes These models facilitate the learning of more complex physical phenomena that govern fuel cell performance and secondly due to the nature of the fuel cell, some of the physical phenomena are obtained in their natural location and are determined by a mathematical model of this information (Cao et al, 2019b). Based on the purpose of design, models can be designed simple or complex (Fei et al, 2019)
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