A parameter optimized model of a Proton Exchange Membrane fuel cell including temperature effects

Experimental measurement of effective diffusion coefficient of gas diffusion layer/microporous layer in PEM fuel cells

“…Region C is the operating range of interest (high current density: >2A/cm 2 ) and is known as Concentration Polarization, where the reaction rate is limited by mass transport through the GDL. Most investigations performed in this area are numerical simulations which can vary from studying the performance and optimization of the cell [7][8][9][10][11][12][13][14] to the mass and heat transfer in the cell [15][16][17][18][19][20][21][22][23]. Experimental research is scarce due to the complexity and difficulty in monitoring the transport mechanisms without interfering with the phenomena, and this raises an issue.…”

Battery Model Parameters Estimation Using Simulated Annealing

“…The operational parameters optimization for fuel economy and lower emission of a Hybrid Electric Vehicle (HEV) is discussed in [23], where a SA algorithm is proposed and its results are compared with the method usually employed for the problem, called dividing rectangles (DIRECT). In [14], a SA method is used for the parameters estimation of an electrical equivalent circuit model of the Proton Exchange Membrane (PEM) fuel cell system. The model is validated by the comparison of experimental and simulated data, with good results agreement.…”

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.…”