The parameter identification of the Nexa 1.2 kW PEMFC's model using particle swarm optimization

Abstract

a b s t r a c tPeople's extensive and ignorant lifestyles impose an increasing amount of destruction on the environment, which lead to an increased governmental and research interest towards the development and use of green technology such as fuel cells. Fuel cells are recently receiving a major share of research interest due to their promising features. This paper presents an offline parameter identification approach based on particle swarm optimization (PSO) to identify the mathematical modeling parameters of the Nexa 1.2 kW proton exchange membrane fuel cell (PEMFC) system. The goal of this work is not to get a new technique in modeling, but rather to obtain a very good model of the PEMFC system using a simple and fast heuristic approach that requires minimal mathematical effort. This model can then be utilized to perform further analysis and fault diagnosis studies on PEMFCs. The proposed approach uses basic fitting to determine some of the initial values for the PSO, while the rest of the initial values are set to be chosen randomly. The developed model is then successfully validated using actual experimental data sets.

Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm

“…Previous works dealing with PEMFC parameters identification have tested PSO [7,26], HADE [12] and EA [25]. HADE is an evolution in parameter identification that surpasses the PSO results and EA was tested to identify the thermal component of a PMFC.…”

“…This last component and the model based on electrical circuits do not let one access the internal signal system. Salim et al [26] used an equations-based model which includes the thermal behavior of a Nexa 1.2 kW PEMFC. The voltage model was developed by fitting a polynomial curve which involves the classical voltage losses.…”

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

“…21,22 In addition, a Markovian switching system is used to regulate DE population size and distribution. 32 A backtracking search optimizer to define PEMFC's polarization curves is presented. 24 Moreover, flower pollination algorithm has been adopted to estimate unknown PEMFC parameters.…”

Membrane‐hydration‐state detection in proton exchange membrane fuel cells using improved ambient‐condition‐based dynamic model

“…PEMFC offers many advantages, such as high efficiency, silent operation, zero greenhouse-gas emission, and most importantly, high energy density. [4][5][6][7][8][9] Most of the aforementioned voltage drops are difficult to calculate, because some model parameters, such as the internal current (I int ), exchange current density (I o ), charge-transfer coefficient (α), and maximum limiting current (I lim ) for a PEMFC vary with ambient temperature and PEMFC loading conditions. 1,2 In PEMFC systems, hydrogen is supplied to the anode while ambient oxygen is supplied to the cathode.…”