Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack

• Published in: Energies (Basel) Vol. 12; no. 5; p. 936
• Main Authors: Luta, Doudou, Raji, Atanda
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2019
• Subjects: Air flow; Air temperature; Algorithms; Amplitudes; boost converter; Capacitors; Chemical reactions; Computer engineering; Computer simulation; Controllers; Converters; Design parameters; Efficiency; Electric potential; Electricity; Electrolytes; Electrolytic cells; Flow rates; Flow velocity; fuel cell; Fuel cells; Fuel technology; Funding; fuzzy logic; Fuzzy systems; Generators; Heat; Hydrogen; International conferences; Molten carbonate fuel cells; MPPT; Operating temperature; Particle Swarm Optimisation; Phosphoric acid; Phosphoric acid fuel cells; Porous materials; Proton exchange membrane fuel cells; Response time; Settling; Solid oxide fuel cells; Spectrum allocation; Voltage; United States > US
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12050936

The negative environmental impact and the rapidly declining reserve of fossil fuel-based energy sources for electricity generation is a big challenge to finding sustainable alternatives. This scenario is complicated by the ever-increasing world population growth demanding a higher standard of living. A fuel cell system is able to generate electricity and water with higher energy efficiency while producing near-zero emissions. A common fuel cell stack displays a nonlinear power characteristic as a result of internal limitations and operating parameters such as temperature, hydrogen and oxygen partial pressures and humidity levels, leading to a reduced overall system performance. It is therefore important to extract as much power as possible from the stack, thus hindering excessive fuel use. This study considers and compares two Maximum Power Point Tracking (MPPT) approaches; one based on the Mamdani Fuzzy Inference System and the other on the Particle Swarm Optimisation (PSO) algorithm to maintain the output power of a fuel cell stack extremely close to its maximum. To ensure that, the power converter interfaced to the fuel cell unit must be able to continuously self-modify its parameters, hence changing its voltage and current depending upon the Maximum Power Point position. While various methods exist for Maximum Power Point tracker design, this paper analyses the response characteristics of a Mamdani Fuzzy Inference Engine and the Particle Swarm Optimisation technique. The investigation was conducted on a 53 kW Proton Exchange Membrane Fuel Cell interfaced to a DC-to-DC boost converter supplying 1.2 kV from a 625 V input DC voltage. The modelling was accomplished using a Matlab/Simulink environment. The results showed that the MPPT controller based on the PSO algorithm presented better tracking efficiency as compared to the Mamdani controller. Furthermore, the rise time of the PSO controller was slightly shorter than the Mamdani controller and the overshoot of the PSO controller was 2% lower than that of the Mamdani controller.