Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm. (1st December 2020)

• Record Type: Journal Article
• Authors: Yang, Zixuan; Liu, Qian; Zhang, Leiyu; Dai, Jialei; Razmjooy, Navid
• Is Part Of: Energy. Volume 212(2020)
• Publication Date: 2020-12-01
• Subjects: Proton exchange membrane fuel cell -- Circuit-based model -- Parameter identification -- The sum of squared error -- Improved barnacles mating optimization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118738
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: 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. Highlights: 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.