Seeker optimization algorithm for global optimization: A case study on optimal modelling of proton exchange membrane fuel cell (PEMFC)

• Published in: International journal of electrical power & energy systems Vol. 33; no. 3; pp. 369 - 376
• Main Authors: Dai, Chaohua, Chen, Weirong, Cheng, Zhanli, Li, Qi, Jiang, Zhiling, Jia, Junbo
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.2011; Elsevier
• Subjects: Applied sciences; Direct energy conversion and energy accumulation; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Energy; Energy. Thermal use of fuels; Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc; Exact sciences and technology; Fuel cells; Global optimization; Human searching behaviors; Optimal modelling; Proton exchange membrane fuel cell; Seeker optimization algorithm; Global optimization; Proton exchange membrane fuel cell; Human searching behaviors; Seeker optimization algorithm; Optimal modelling; State of the art; Polymer solid electrolyte; System design; Global optimum; Algorithm; Particle swarm optimization; Case study; Fuel systems; Safe operating area; System simulation; Cell system; System analysis; Proton exchange membrane fuel cells; Comparative study; Fuel cell
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2010.08.032

In order to optimize the proton exchange membrane fuel cell (PEMFC) model parameters, a novel approach based on seeker optimization algorithm (SOA) is proposed. The SOA is based on the concept of simulating human searching behaviors, where the choice of search direction is based on the empirical gradient by evaluating the response to the position changes and the decision of step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, after evaluated on benchmark function optimization, the SOA is applied to optimal modelling of the PEMFC by using a fuel cell test system in Fuel Cell Application Centre (FAC) at the Temasek Polytechnic, and compared with several state-of-the-art versions of differential evolution (DE) and particle swarm optimization (PSO) algorithms. The simulation results show that the proposed approach is superior to other compared algorithms, and the PEMFC model with optimized parameters by SOA fitted experimental data well. Hence, SOA is an effective and reliable technique for optimizing the parameters of PEMFC model, and can be helpful for system analysis, optimization design and real-time control of the PEMFCs.