PEM Fuel Cell System Identification and Control

Kumar, Pinagapani Arun; Mani, Geetha; Chandran, Karthik; Sanjeevikumar, P.

doi:10.1007/978-981-10-4286-7_44

Cited by 3 publications

(1 citation statement)

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“…Panagiotis et al performed parametric analysis on two dynamic fuel cell models in which one is based on electrical equivalent and the other uses a semi-empirical approach [7]. A real-time model is designed and validated using various techniques by Kumar et al [8]. Meanwhile, a study shifted the attention to improving the fuel cells' stability and performance by enhancing the humidification of the polymer electrolyte membrane of PEMFCs [9].…”

Section: Introductionmentioning

confidence: 99%

Parameters Identification of Proton Exchange Membrane Fuel Cell Model Based on the Lightning Search Algorithm

et al. 2022

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The fuel cell is vital in electrical distribution networks as a distributed generation in today’s world. A precise model of a fuel cell is extensively required as it rigorously affects the simulation studies’ transient and dynamic analyses of the fuel cell. This appears in several microgrids and smart grid systems. This paper introduces a novel attempt to optimally determine all unknown factors of the polymer exchange membrane (PEM) fuel cell model using a meta-heuristic algorithm termed the Lightning search algorithm (LSA). In this model, the current–voltage relationship is heavily nonlinear, including several unknown factors because of the shortage of fuel cell data from the manufacturer’s side. This issue can be treated as an optimization problem, and LSA is applied to detect its ability to solve this problem accurately. The objective function is the sum of the squared error between the estimated output voltage and the measured output voltage of the fuel cell. The constraints of the optimization problem involve the factors range (lower and upper limit). The LSA is utilized in minimizing the objective function. The effectiveness of the LSA-PEM fuel cell model is extensively verified using the simulation results performed under different operating conditions. The simulation results of the proposed model are compared with the measured results of three commercial fuel cells, such as Ballard Mark V 5 kW, BCS 500 W and Nedstack PS6 6 kW, to obtain a realistic study. The results of the proposed algorithm are also compared with different optimized models to validate the model and, further, to determine where LSA stands in terms of precision. In this regard, the proposed model can yield a lower SSE by more than 5% in some cases and high performance of the LSA-PEMFC model. With the results obtained, it can be concluded that LSA prevails as a potential optimization algorithm to develop a precise PEM fuel cell model.

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Section: Introductionmentioning

confidence: 99%

Parameters Identification of Proton Exchange Membrane Fuel Cell Model Based on the Lightning Search Algorithm

et al. 2022

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Optimal parameter extraction of PEM fuel cells by meta-heuristics

Zhang

Xiao

Razmjooy

2020

International Journal of Ambient Energy

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Optimization of PEMFC Model Parameters Using Meta-Heuristics

et al. 2021

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The present study introduces an economical–functional design for a polymer electrolyte membrane fuel cell system. To do so, after introducing the optimization problem and solving the problem based on the presented equations in the fuel cell, a cost model is presented. The final design is employed for minimizing the construction cost of a 50 kW fuel cell stack, along with the costs of accessories regarding the current density, stoichiometric coefficient of the hydrogen and air, and pressure of the system as well as the temperature of the system as optimization parameters. The functional–economic model is developed for the studied system in which all components of the system are modeled economically as well as electrochemically–mechanically. The objective function is solved by a newly improved metaheuristic technique, called converged collective animal behavior (CCAB) optimizer. The final results of the method are compared with the standard CAB optimizer and genetic algorithm as a popular technique. The results show that the best optimal cost with 0.1061 $/kWh is achieved by the CCAB. Finally, a sensitivity analysis is provided for analyzing the consistency of the method.

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PEM Fuel Cell System Identification and Control

Cited by 3 publications

References 1 publication

Parameters Identification of Proton Exchange Membrane Fuel Cell Model Based on the Lightning Search Algorithm

Parameters Identification of Proton Exchange Membrane Fuel Cell Model Based on the Lightning Search Algorithm

Optimal parameter extraction of PEM fuel cells by meta-heuristics

Optimization of PEMFC Model Parameters Using Meta-Heuristics

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