Abstract
The output power of a fuel cell mainly depends on the operating conditions such as cell temperature and membrane water content. The fuel cell (FC) power versus FC current graph has a unique maximum power point (MPP). The location of the MPP is variable, depending on the operating condition. Consequently, a maximum power point tracker (MPPT) is highly required to ensure that the fuel cell operates at an MPP to increase its performance. In this research work, a variable step-size incremental resistance (VSS-INR) tracking method was suggested to track the MPP of the proton exchange membrane (PEMFC). Most of MPPT methods used with PEMFC require at least three sensors: temperature sensor, water content sensor, and voltage sensor. However, the proposed VSS-INR needs only two sensors: voltage and current sensors. The step size of the VSS-INR is directly proportional to the error signal. Therefore, the step size will become small as the error becomes very small nearby the maximum power point. Accordingly, the accuracy of the VSS-INR tracking method is high in a steady state. To test and validate the VSS-INR, nine different scenarios of operating conditions, including normal operation, only temperature variation, only variation of water content in the membrane, and both variations of temperature and water content simultaneously, were used. The obtained results were compared with previously proposed methods, including particle swarm optimization (PSO), perturb and observe (P&O), and sliding mode (SM), under different operating conditions. The results of the comparison confirmed the superiority of VSS-INR compared with other methods in terms of the tracking efficiency and steady-state fluctuations.
Highlights
The use of fossil fuels has been limited due to its negative effects on the environment as they cause global warming
fuel cell (FC) which are hydrogen-based storage systems are recommended as alternatives to batteries
Ahmadi et al [7] represented an maximum power point tracker (MPPT) for the proton exchange membrane fuel cell (PEMFC) via a PID controller designed by particle swarm optimization (PSO); the output of the controller was the duty cycle fed to a boost converter for extracting the maximum power
Summary
The use of fossil fuels has been limited due to its negative effects on the environment as they cause global warming. Fathy et al [3] presented an MPPT based on a metaheuristic optimization approach of a salp swarm algorithm (SSA) to extract the maximum power from the PEMFC; the SSA was employed to optimize a proportional-integral-derivative (PID) controller. A smart drive algorithm has been presented by Derbeli et al [6] to simulate an MPPT with a boost converter to catch the maximum power of the PEMFC. Ahmadi et al [7] represented an MPPT for the PEMFC via a PID controller designed by particle swarm optimization (PSO); the output of the controller was the duty cycle fed to a boost converter for extracting the maximum power. Harrabi et al [11] introduced a Fuzzy logic controller constructed based on an IC technique to simulate an MPPT incorporated in a PV/FC system via a controlling buck converter. Reddy et al [29] introduced an ANFIS-based MPPT for the PEMFC powering
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
