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

Abstract

Demonstrating an accurate mathematical model is a mandatory issue for realistic simulation, optimization and performance evaluation of proton exchange membrane fuel cells (PEMFCs). The main goal of this study is to demonstrate a precise mathematical model of PEMFCs through estimating the optimal values of the unknown parameters of these cells. In this paper, an efficient optimization technique, namely, Tree Growth Algorithm (TGA) is applied for extracting the optimal parameters of different PEMFC stacks. The total of the squared deviations (TSD) between the experimentally measured data and the estimated ones is adopted as the objective function. The effectiveness of the developed parameter identification algorithm is validated through four case studies of commercial PEMFC stacks under various operating conditions. Moreover, comprehensive comparisons with other optimization algorithms under the same study cases are demonstrated. Statistical analysis is presented to evaluate the accuracy and reliability of the developed algorithm in solving the studied optimization problem.

Improved Metaheuristic Algorithms for Optimal Parameters Selection of Proton Exchange Membrane Fuel Cells: A Comparative Study

“…The standard GWO was hybridised with crossover and mutation operators in this study to enhance its ability to explore while avoiding getting trapped in local optima. Tree Growth Algorithm (TGA) [46] 2020 To create a precise mathematical model of PEMFCs, a technique known as the Tree Growth algorithm was presented in this study to estimate the seven unknown parameters based on minimizing the total squared deviations (TSD) between the empirically measured data and the estimated data. Equilibrium Optimizer (EO) [47] 2020…”

“…This commercial PEMFC module is widely used in the literature to assess the performance of the optimization techniques used for estimating the values of its unidentified parameters under the following structural parameters and operating condition setting: N cells = 32, A cm 2 = 64, l (µm) = 178, J max ( mA cm 2 ) = 469,T f c (K)=333, P O2 (atm) = 0.2095, and P H2 (atm) = 1.0 [29], [46]. For estimating the unknown parameters of this test case, all algorithms have been independently executed 40 times and the best, average (Avg), and worst SSE values, in addition to their standard deviation (SD) to show the stability for each algorithm have been calculated and presented in Table 1, which shows that HSOA, HHOA, and HGBO could be competitive with each other and superior to the other compared optimizers in terms of the best SSE; however, for the Avg SSE value, the best MAE and MAPE values, both HSOA and HGBO could come true the same outcomes that are the best in comparison to those obtained by the others algorithms.…”

Fuel-cell parameter estimation based on improved gorilla troops technique

“…In addition to that, artificially ecosystem optimization 27 , tree-seed algorithm (TSA) and neural network algorithm 28 have been applied for the same issue. Also, the researchers utilized the similar context of tree-growth algorithm 29 , flower pollination method 30 , political optimization algorithm, marine predator technique 31 and slime mould optimization algorithm 32 for parameter identification of PEMFCs. In 33 , a combination between teaching learning based optimizer and DE approach has been developed while a modified salp swarm optimizer has been presented to identify the optimal PEMFCs stack parameters in 34 .…”

Proton Exchange Membrane Fuel Cell Parameter Extraction Using a Supply–Demand-Based Optimization Algorithm

“…The great advantages of using metaheuristic algorithms are the simplicity, ease of implementation, and robustness [15]. The broad applications of metaheuristic algorithms in solving the parameter identification problems of PEMFC models, such as the genetic algorithm (GA) [16], particle swarm optimization (PSO) [17], firefly optimization (FFO) [18], grey wolf optimization (GWO) [19], simulated annealing (SA) [20], harmony search (HS) [21], artificial bee swarm (ABS) optimization [22], flower pollination algorithm (FPA) [23], artificial bee colony (ABC) algorithm [24], big bang-big crunch (BBBC) algorithm [25], salp swarm optimizer (SSA) [26], shark smell optimizer (SSO) [27], multiverse optimizer (MVO) [28], teaching learning-based algorithm (TLBO) [29], backtracking search algorithm (BSA) [30], differential evolution algorithm (DEA) [31], biogeographybased optimization (BBO) [32], imperialist competitive algorithm (ICA) [33], grasshopper optimization algorithm (GOA) [34], bird mating optimizer (BMO) [35], flower pollination algorithm (FPA) [23], whale optimization algorithm (WOA) [36], satin bowerbird optimizer (SBO) [37], seagull optimization algorithm (SOA) [38], shuffled frog-leaping algorithm (SFLA) [33], vortex search algorithm (VSA) [39], bat algorithm (BA) [40], owl search algorithm (OSA) [18], tree growth algorithm (TGA) [41], Harris hawks optimization (HHO) [42], atom search optimizer (ASO) [43], dragonfly algorithm (DA) [44], ant lion optimizer (ALO) [44], cuckoo search algorithm (CS) [45], artificial ...…”