PSO based PI controller design for a solar charger system

Area of Science:

• Renewable Energy Systems
• Electrical Engineering
• Materials Science

Background:

• Photovoltaic (PV) systems are crucial for addressing global energy and environmental concerns.
• Existing solar charger designs often separate Maximum Power Point Tracking (MPPT) and battery charge control.
• Optimizing both MPPT and Li-ion battery charging simultaneously is key for efficient portable power.

Purpose of the Study:

• To develop a two-stage system for simultaneous MPPT and optimal Li-ion battery charge control.
• To enhance the efficiency and performance of portable solar energy charging systems.
• To compare intelligent algorithms for optimizing PI charge controller parameters.

Main Methods:

• Designed a DC/DC boost converter with Variable Step Size Incremental Conductance (VSINC) for real-time MPPT.
• Utilized a DC/DC buck converter to regulate voltage for Li-ion battery charging via Constant Current/Constant Voltage (CC/CV) method.
• Employed intelligent algorithms, specifically Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs), to determine optimal PI charge controller parameters.

Main Results:

• The VSINC method effectively enabled solar cells to track the maximum power point.
• The two-stage system successfully implemented simultaneous MPPT and optimal Li-ion battery charging.
• PSO-derived control parameters demonstrated superior performance compared to GAs in simulations and experiments.

Conclusions:

• The proposed two-stage system offers an efficient solution for portable solar energy charging.
• Intelligent algorithms, particularly PSO, are effective in optimizing charge controller parameters for enhanced performance.
• This integrated approach advances the development of efficient and reliable renewable energy solutions.