An efficient modified artificial electric field algorithm for solving optimization problems and parameter estimation of fuel cell

Abstract

The artificial electric field algorithm (AEFA) is a recent physics populationbased optimization approach inspired by Coulomb's law of electrostatic force and Newton's law of motion. In this paper, an alternative version of AEFA called mAEFA is proposed to boost the searchability and the balance between the explorations to the exploitation of the original AEFA. To escape dropping on the local points in the mAEFA, three efficient strategies for instance; modified local escaping operator (MLEO), levy flight (LF), and opposition-based learning (OBL), are in conjunction with the original AEFA. The convergence rate will be improved when the best agent is identified; thus, stagnation at a local solution can be efficiently avoided. To assess the performance of the proposed mAEFA, it has been evaluated over the CEC'2020 test functions. Furthermore, a robust methodology based on mAEEA is proposed to identify the best parameters of PEM fuel cell (PEMFC). The model of the PEMFC includes nonlinear characteristics that involve several unknown design variables. Thus, it is challenging to develop an accurate model. There are seven design variables to be tuned to reach the targeted dependable model. Two different types of PEMFCs: NedStack PS6 and SR-12 500 W were used to demonstrate the superiority of the mAEEA. Throughout the optimization process, the unidentified parameters of PEMFC are appointed to be decision variables. But the objective function, which necessary to be least is represented by the SSE between the calculated PEMFC voltage and the experimental one. Nine recent optimizers are used in the comparison with the proposed mAEEA. According to the main findings, the advantage of the proposed mAEEA in determining the best PEMFC parameters is verified compared to the other optimizers. Lowest SSE, lowest RMSE, minimum stranded deviation, maximum efficiency, and high coefficient of determination are achieved by the proposed mAEEA.

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

“…Furthermore, the following parameters of ξ 1 , ξ 2 , ξ 3 , ξ 4 , β, R C , and λ should be accurately estimated through the tested optimisation algorithms. The estimation process considers the search ranges of Table 1 for each parameter [7–9, 13, 15, 16, 19, 22, 29, 42].…”

“…Many optimisation algorithms have been applied with the purpose of drawing out the parameters of the PEMFCs [6,8,9,11,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Moreover, the application of recent optimisa-tion algorithms is very interesting by many researchers for modelling the components of energy systems, such as PV cells [30,31].…”

Accurate PEM fuel cells parameters estimation using hybrid artificial bee colony differential evolution shuffled complex optimizer

“…Hybridized algorithms are practical solution to well enhance the meta‐heuristic technique performances 28 . For instance, the following hybrid algorithms have been recently validated to obtain more reliability with less computational time: teaching learning‐based optimization and differential evaluation, 23 hybrid adaptive differential evolution, 29 cuckoo search algorithm with explosion operator, 15 circular genetic operators based RNA‐GA, 30 biogeography‐based optimization incorporated with differential evolution, 31 vortex search approach and differential evaluation (VSDE), 22 improved chaotic MayFly optimization algorithm (CMOA), 32 LSHADE‐EpSin, 33 and modified artificial electric field algorithm (mAEFA) 34 …”

“…28 For instance, the following hybrid algorithms have been recently validated to obtain more reliability with less computational time: teaching learning-based optimization and differential evaluation, 23 hybrid adaptive differential evolution, 29 cuckoo search algorithm with explosion operator, 15 circular genetic operators based RNA-GA, 30 biogeography-based optimization incorporated with differential evolution, 31 vortex search approach and differential evaluation (VSDE), 22 improved chaotic MayFly optimization algorithm (CMOA), 32 LSHADE-EpSin, 33 and modified artificial electric field algorithm (mAEFA). 34 However, most of the abovementioned metaheuristic techniques are not able to accurately identify the unknown parameters with regard to the mean standard deviation (STD) and the CPU run time (CRT) simultaneously. As it is still a marge of improvement, developing new framework could strengthen the identified parameters accurately with interesting convergence performances.…”

An Efficient Parameter Estimation Algorithm for Proton Exchange Membrane Fuel Cells

“…There are several other parameter estimation PEMFC techniques based on metaheuristic algorithms: improved chaotic grey wolf optimization algorithm [26], modified farmland fertility optimizer [18], hunger games search algorithm [27], improved version of the Archimedes optimization algorithm [28], moth-flame optimization [19], Levenberg-Marquardt backpropagation algorithm [29], whale optimization algorithm [30], marine predator algorithm optimizer [31], pathfinder algorithm [32], hybrid water cycle moth-flame optimization algorithm [33], improved fluid search optimization algorithm [34], Seeker optimization algorithm [35], improved grass fibrous root optimization algorithm [36], developed coyote optimization algorithm [37], improved TLBO with elite strategy [38], developed owl search algorithm [39], modified artificial electric field algo-Energies 2021, 14, 7115 5 of 23 rithm [40], Supply-Demand-Based Optimization Algorithm [41], convolutional neural network optimized by balanced deer hunting optimization algorithm [42], and chaos game optimization technique [43].…”