Predictive Maximum Power Point Tracking with Newton Raphson Technique for Proton Exchange Membrane Fuel Cell
The proton exchange membrane fuel cell (PEMFC) is a renewable energy source known for its high- power density, quick start-up, and low operating temperature. However, due to internal parameters or load resistance, the PEMFC may not always produce electrical power at the maximum power point (MPP)....
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Semarak Ilmu Publishing
2025
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/49042/1/ARFMTSV133_N1_P37_58.pdf http://ir.unimas.my/id/eprint/49042/ https://semarakilmu.com.my/journals/index.php/fluid_mechanics_thermal_sciences/article/view/12543 https://doi.org/10.37934/arfmts.133.1.3758 |
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| Summary: | The proton exchange membrane fuel cell (PEMFC) is a renewable energy source known for its high- power density, quick start-up, and low operating temperature. However, due
to internal parameters or load resistance, the PEMFC may not always produce electrical power at the maximum power point (MPP). This paper suggests a modified finite control
set model predictive control (FCS-MPC) approach to achieve maximum power for PEMFC by modulating the duty cycle. The predictive control scheme and the Modified Newton-
Raphson method are combined to converge towards the current at the MPP specifically, which has not been widely explored in the maximum power point tracking (MPPT) technique. The duty cycle of the DC-DC boost converter is controlled by the predictive MPPT system to regulate the output voltage of the PEMFC. Simulation results show that the proposed MPPT technique can track the MPP for various fuel cell (FC) parameters with a tracking speed of MPP within 0.012 s. It is faster than most metaheuristic optimization approaches, indicating less energy loss. Additionally, it demonstrates excellent accuracy of up to 99.11% and is resistant to changes in internal PEMFC parameters. |
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