Design and development of different adaptive MPPT controllers for renewable energy systems: a comprehensive analysis

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Automotive Technology

Background:

• Electric vehicles (EVs) are gaining traction globally due to environmental benefits and lower running costs.
• Proton Exchange Membrane Fuel Cells (PEMFCs) offer fast startup and quick response, making them suitable for EV power sources.
• PEMFCs exhibit nonlinear voltage-current characteristics, complicating maximum power extraction.

Purpose of the Study:

• To investigate and compare various Maximum Power Point Tracking (MPPT) techniques for DC-DC converter-fed PEMFC systems.
• To evaluate the performance of different MPPT controllers in optimizing power output from PEMFCs.

Main Methods:

• The study examined six MPPT controllers: Adjusted Step Value of Perturb & Observe (ASV with P&O), Adaptive Step Size with Incremental Conductance (ASS with IC), Radial Basis Functional Network (RBFN), Incremental Step-Fuzzy Logic Controller (IS with FLC), Continuous Step Variation based Particle Swarm Optimization (CSV with PSO), and Adaptive Step Value-Cuckoo Search Algorithm (ASV with CSA).
• Performance was assessed based on maximum power extraction, tracking speed, settling time, output voltage oscillations, and design complexity.

Main Results:

• The Adaptive Step Value with Cuckoo Search Algorithm (ASV with CSA) demonstrated superior performance compared to other evaluated MPPT techniques.
• ASV with CSA showed enhanced efficiency in extracting maximum power and faster response times.

Conclusions:

• The ASV with CSA technique is highly effective for maximizing power output from PEMFCs in EV applications.
• Optimized MPPT control is crucial for efficient operation of fuel cell-powered electric vehicles.