A new parameter extraction method for accurate modeling of PEM fuel cells

Abstract

SUMMARYIn this paper, a new parameter extraction method for accurate modeling of proton exchange membrane (PEM) fuel cell systems is presented. The main difficulty in obtaining an accurate PEM fuel cell dynamical model is the lack of manufacturer information about the exact values of the parameters needed for the model. In order to obtain a realistic dynamic model of the PEM system, the electrochemical considerations of the system are incorporated into the model. Although many models have been reported in the literature, the parameter extraction issue has been neglected. However, model parameters must be precisely identified in order to obtain accurate simulation results. The main contribution of the present work is the application of the simulated annealing (SA) optimization algorithm as a method for identification of PEM fuel cell model parameter identification. The major advantage of SA is its ability to avoid becoming trapped in local minimum, as well as its flexibility and robustness. The parameter extraction and performance validation are carried out by comparing experimental and simulated results. The good agreement observed confirms the usefulness of the proposed extraction approach together with adopted PEM fuel cell model as an efficient tool to help design of power fuel cell power systems.

Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm

“…To conquer this drawback, metaheuristic algorithms such as Genetic Algorithms (GA) [20][21][22][23][24][25], Simulated Annealing (SA) [26,27], Differential Evolution (DE) [28,29], Particle Swarm Optimization (PSO) [30,31], Artificial Immune System (AIS) [5], Seeker Optimization Algorithm (SOA) [32], Harmony Search (HS) [33,34], Hybrid Artificial Bee Colony (HABC) [19], Artificial Bee Swarm Algorithm (ABSA) [35], P System Based Optimization (PSBO) [36], Teaching-learning-based optimization (TLBO) [37], Biogeography-based optimization [38] and Bird Mating Optimization (BMO) [39] have been applied in this problem. Metaheuristics generally do not need domain information and they are derivative free methods which perform stochastic movements to obtain global optimum point.…”

Modeling and optimization for proton exchange membrane fuel cell stack using aging and challenging P systems based optimization algorithm

“…Heuristic algorithms are more suitable tools because they are stochastic and derivative-free (do not need close forms) techniques having effective global search ability [12]. In the literature, many of them were used to deal with parameters estimation issues, such as GA (genetic algorithm) [7,10,11,17], PSO (particle swarm optimization) [13,18], DE (differential evolution) [19,20], SA (simulated annealing) [21,22], artificial bee algorithm [14,23], HS (harmony search) [15,16], P systems based optimization [24]， etc. Among them, bio-inspired computing has been proven to be the promising research direction.…”

Parameter Estimation for a Proton Exchange Membrane Fuel Cell Model Using GRG Technique

“…So far, various phenomenon‐mimicking algorithms, such as genetic algorithm 1, 3, particle swarm optimization 5, 6, simulated annealing 7, and harmony search 4, have been applied for parameter estimation of the PEMFC model. However, there is still chance to produce good results with respect to model accuracy using another technique proposed in this study.…”