Effective Parameter Extraction of Different Polymer Electrolyte Membrane Fuel Cell Stack Models Using a Modified Artificial Ecosystem Optimization Algorithm

Abstract

Recently, extracting the precise values of unknown parameters of the polymer electrolyte membrane fuel cell (PEMFC) is considered one of the most widely nonlinear and semi-empirical optimization problems. This paper proposes and applies a Modified Artificial Ecosystem Optimization (MAEO) algorithm to solve the problem of PEMFC parameters extraction. The conventional AEO is a novel optimization technique that is inspired by the energy flow in a natural ecosystem which is defined as abiotic, which includes non-living bodies and elements such as light, water and air. The proposed optimization algorithm, MAEO, is used to enhance the performance of conventional AEO and provide faster convergence rate as well as to be far away from falling into the local optima. In the proposed MAEO, an operator is suggested to improve the balance between exploitation and Exploration phases. The accurate estimation of PEMFC unknown parameters leads to develop a precise mathematical model which simulates the electrochemical and electrical characteristics of PEMFC. The objective function of the studied optimization problem is formulated as the sum of squared errors (SSE) between the measured and simulated stack voltages. To prove the reliability and capability of the proposed MAEO algorithm in solving this problem compared with other recent algorithms, it is tested on four different PEMFC stack models, namely, BCS-500W, SR-12 500W, 250W and Temasek 1 kW stacks. Moreover, statistical measures are performed to assess the superiority and robustness of the proposed algorithm. In addition, the accuracy of optimized parameters is assessed through the dynamic characteristics of PEMFCs under varying the reactants' pressures and temperature of the cell. However, the simulation results confirm that the proposed MAEO algorithm has high accuracy and reliability in extracting the PEMFC optimal parameters compared with the conventional AEO and other effective algorithms. INDEX TERMS Polymer electrolyte membrane fuel cell, parameters extraction, modified artificial ecosystem optimization, sum of squared errors, polarization curves.

An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations

“…This algorithm has been tested on 31 mathematical benchmark functions and 8 realistic engineering design problems. The obtained results in [22], proved that the performance of the AEO outperformed other optimization techniques. Consequently, it has been applied in solving few optimization problems.…”

“…These types include evolutionary algorithms such as genetic algorithm (GA) [10] and differential evolution (DE) [11]; swarm intelligence such as particle swarm optimization (PSO) [12], ant colony optimization (ACO) [13] and artificial bee colony optimization (ABC) [14]; physical algorithms such as gravitational search algorithm (GSA) [15] and wind-driven optimization (WDO) [16]; bio algorithms such as grey wolf optimizer (GWO) [17] and bacterial colony foraging optimization (BCFO) [18]; and others such as sine cosine algorithm (SCA) [19], differential search algorithm (DSA) [20] and harmony search (HS) [21]. New algorithms have been added recently to this list, such as artificial ecosystem-based optimization (AEO) [22], [23], Levy flight distribution (LFD) algorithm [24], turbulent flow of water-based optimization (TFWO) [25] and atomic search optimization (ASO) [26]. All the artificial intelligence optimization techniques can solve any engineering problem even if it is multidimensional, non-continuous, or nondifferentiable.…”

“…AEO has been recently proposed as a nature-inspired metaheuristic optimization algorithm [22]. It is a population-based optimization technique inspired by the energy flow in an ecosystem on the earth.…”

Observations on the Parameter Estimation Problem of Polymer Electrolyte Membrane Fuel Cell Polarization Curves

“…In contrast, some of the reviewed studies fail to take this into consideration, and even compare the objective function values gained with different parameter sets (i.e., model structures), parameter search space, and even with different objective functions. Some of the most recent studies have noticed the missing tabulated data sets and have presented the data used [9,13,36,53,[64][65][66]. Also, the effect of the measurement errors is covered in [56,59,62], where a noise component was added to the data.…”

Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models

“…The differential evaluation (DE), as well as the hybrid adaptive differential evaluation (HADE) algorithms, have been introduced for solving the optimization problem presented in [22], [23]. More recent optimization methods have been applied to solve the problem of PEMFC's parameter such as: the harmony search algorithm (HAS) [24], the seeker optimization algorithm (SOA) [25], the multi-verse optimizer (MVO) [26], the adaptive RNA genetic algorithm [27], Eagle strategy based on JAYA algorithm and Nelder-Mead simplex method (JAYA-Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models NM) [28], grey wolf optimizer (GWO) [29], hybrid Teaching Learning Based Optimization -Differential Evolution algorithm (TLBO-DE) [30], shark smell optimizer (SSO) [25], Cuckoo search algorithm with explosion operator (CS-EO) [31], selective hybrid stochastic strategy [32], bird mating optimizer [33], grasshopper optimizer (GHO) [34], Chaotic Harris Hawks optimization (CHHO) [35], and Modified Artificial Ecosystem Optimization (MAEO) [36].…”