Maximum power point tracking control method for proton exchange membrane fuel cell

Proton Exchange Membrane FC (PEMFC) is widely recognized as a potentially renewable and green energy source based on hydrogen. Maximum power point tracking (MPPT) is one of the most important working conditions to be considered. In order to improve the searching performance such as convergence and robustness under disturbance and uncertainty, a kind of fractional order low pass filter (FOLPF) is applied for the MPPT controller design based on general Extremum Seeking Control (ESC). The controller is designed with FOLPF and high pass filter (HPF) substituting the normal LPF and HPF in the original ESC design. With this FOLPF ESC, better convergence and smooth performance is gained while maintaining the robust specifications. Simulation results are included to validate the proposed new FOLPF ESC scheme under disturbance and comparisons between FOLPF ESC and general ESC method are also provided.

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Maximum power point tracking of a proton exchange membrane fuel cell system using PSO-PID controller

International Journal of Hydrogen Energy ◽

10.1016/j.ijhydene.2017.06.208 ◽

2017 ◽

Vol 42 (32) ◽

pp. 20430-20443 ◽

Cited By ~ 32

Author(s):

S. Ahmadi ◽

Sh. Abdi ◽

M. Kakavand

Keyword(s):

Pid Controller ◽

Proton Exchange Membrane ◽

Cell System ◽

Proton Exchange ◽

Fuel Cell System ◽

Maximum Power Point ◽

Point Tracking ◽

Power Point Tracking ◽

Power Point ◽

Exchange Membrane

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Novel Neural network single sensor MPPT for Proton Exchange Membrane Fuel Cell

Journal of New Materials for Electrochemical Systems ◽

10.14447/jnmes.v24i1.a08 ◽

2021 ◽

Vol 24 (1) ◽

pp. 43-48

Author(s):

Abdelghani Harrag

Keyword(s):

Neural Network ◽

Fuel Cell ◽

Proton Exchange Membrane ◽

Proton Exchange ◽

Maximum Power ◽

Maximum Power Point ◽

Point Tracking ◽

Single Sensor ◽

Power Point ◽

Exchange Membrane

This paper presents a new neural network single sensor maximum power point tracking algorithm controlling the DC-DC boost converter to guarantee the transfer of the proton exchange membrane fuel cell maximum generated power to the load. The implemented neural network single sensor controller has been developed and trained firstly in offline mode using single sensor maximum power point tracking data obtained previously; and secondly used in online mode to track the maximum output power of the fuel cell power system. Comparative simulation results prove the superiority of the proposed neural network single sensor maximum power point compared to the single sensor one especially in transit response reducing by the way the overshoot and the tracking time which leads to an overall energy losses reduction. In addition, the implemented neural network single sensor MPPT employs only one sensor which will reduce the complexity and the cost of PEM fuel cell power system. To our knowledge, this study is a pioneering work using a neural network single sensor controller as PEM fuel cell MPPT.

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