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In this paper, a circuit-based model of proton exchange membrane fuel cell (PEMFC) is developed for optimal selection of the model parameters. The optimization is based on using an improved version of Monarch Butterfly Optimization (IMBO) algorithm for minimizing the Integral Time Absolute Error between the measured output voltage and the output voltage of the achieved model. For validation of the proposed method, two different case studies including 6 kW NedSstack PS6 and 2 kW Nexa FC PEMFC stacks have been employed and the results have been compared with the experimental data and some well-known metaheuristics including Chaotic Grasshopper Optimization Algorithm (CGOA), Grass Fibrous Root Optimization Algorithm (GRA), and basic Monarch Butterfly Optimization (MBO) to indicate the superiority of the proposed method against the compared methods. Final results show a satisfying agreement between the proposed IMBO and the experimental data. • A new technique proposed for optimal selection of PEMFC parameters • The method is based on a new model of Coyote Optimization Algorithm • The achieved results showed a good confirmation by the experimental data [ABSTRACT FROM AUTHOR]

This paper describes the procedure to qualify an admittance meter as a permittivity meter and a conductivity meter σsensor through validation with liquids over a range of relative permittivity εr values from 1 to 80 and with operating frequencies f between 1 and 20 MHz. The sensor is a capacitor which consists of two parallel cylinders of 10-cm typical dimension. A circuit-based model of the sensor was previously calibrated against accurate electronic components. A discrepancy of 10% is found between the calibration with components and the validation with liquids. All potential errors have been carefully examined: parasitic impedances of the electronic circuit and leads, modifications of liquid permittivity εrliq due to temperature influence, water contamination or relaxation effects, and possible fringing effects with the help of numeric simulations. Forty percent of the discrepancy results from the finite dimensions of the liquid-filled recipient. The fringing effects due to insulating rings at each end of the capacitor seem to be discarded. The analysis of the uncertainty on εrsensor and σsensor shows a relative uncertainty of 3%-5% due in large part to the numerical acquisition of high frequency. [ABSTRACT FROM PUBLISHER]

In this paper, a new optimal method is proposed to select unknown parameters of the proton exchange membrane fuel cell (PEMFC) models. The method was based on minimizing the sum of squared error (SSE) value between the experimental output voltage and the estimated output voltage for the PEMFC stack. The minimization is based on employing a new improved design of the Barnacles Mating Optimization (IBMO) algorithm for increasing the system accuracy and robustness. The method is then validated based on two different case studies, including Horizon 500W PEMFC and NedSstack PS6 PEMFCs by comparing its results by the real data and also some well-known methods including Emperor Penguin Optimizer (EPO), Elephant Herding behavior Optimization (EHO) Algorithm, and world cup optimization algorithm (WCO). The results show that the suggested IBMO with 2.11 SSE has the minimum error and the EPO, EHO, and the WCO with 2.13, 2.26, and 2.29 SSE are in the next ranks. Also, for the Horizon 500W, the SSE value of the IBMO, EPO, WCO, and BMO are 0.012, 0.019, 0.029, and 0.031, respectively which shows the suggested method's superiority. Simulation results indicate that the suggested method has the best agreement with the empirical data. • The new optimal method is proposed to select unknown parameters of the PEMFCs. • The method was based on minimizing the sum of squared error (SSE). • The aim is to minimize the SSE between the real and estimated output voltage. • The method is validated by two different case studies. [ABSTRACT FROM AUTHOR]