Maximum power point tracking of PEMFC based on hybrid artificial bee colony algorithm with fuzzy control

Area of Science:

• Renewable Energy Systems
• Control Theory
• Electrical Engineering

Background:

• Proton exchange membrane fuel cells (PEMFCs) require efficient power management.
• Nonlinear characteristics of PEMFCs challenge conventional maximum power point tracking (MPPT) methods.
• Existing MPPT algorithms often suffer from local optimization, slow convergence, and large overshoots.

Purpose of the Study:

• To develop an advanced MPPT control scheme for PEMFCs.
• To improve the power generation efficiency and power supply quality of PEMFCs.
• To overcome the limitations of conventional MPPT methods.

Main Methods:

• Proposed a hybrid MPPT control scheme combining the artificial bee colony (ABC) algorithm with fuzzy control (ABC-fuzzy).
• Utilized the global optimization capability of the ABC algorithm to find the maximum power point.
• Employed fuzzy control to mitigate overshoot and accelerate convergence speed of the ABC algorithm.

Main Results:

• The ABC-fuzzy method demonstrated superior performance over perturb & observe, conductance increment, and basic ABC methods.
• Achieved greater output power, faster regulation speed, and reduced steady-state error for PEMFCs.
• Showcased enhanced anti-interference ability and reduced oscillation compared to other methods.

Conclusions:

• The ABC-fuzzy MPPT scheme effectively maximizes PEMFC output power.
• This approach significantly improves the service life and power supply efficiency of PEMFCs.
• The proposed method offers a robust solution for efficient PEMFC operation.