Intelligence artificial neural network (IANN) and Fuzzy Logic Control (FLC) Integration for an Optimized Lithium-ion Battery Charging Circuit for Photovoltaic Systems

Abstract

In hybrid renewable energy systems, batteries play a crucial role in energy storage, particularly in configurations powered by photovoltaic (PV) technology. Under stable environmental conditions, PV systems can operate independently to generate electricity. However, during fluctuating or unstable conditions, batteries collaborate with the PV system to ensure a consistent and reliable energy supply. This article proposes the design of a smart charging circuit for lithium-ion batteries, capable of dynamically adjusting to extract maximum power from the PV system—even when sunlight intensity varies from 0 to 1000 W/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, while maintaining a constant temperature of 25°C. The proposed system employs two intelligent control strategies: an improved artificial neural network (IANN) and a fuzzy logic controller (FLC). These techniques are applied to control a DC-DC converter, ensuring that the output voltage from the solar panels is precisely regulated to match the battery’s nominal charging voltage. The integration of IANN and FLC enhances the system’s ability to quickly adapt to changing solar conditions, thereby improving both energy capture and battery charging efficiency. A comprehensive simulation study conducted using MATLAB/Simulink validates the performance of the proposed system. The results confirm its effectiveness in real-time operation under dynamic environmental conditions, demonstrating notable improvements in energy utilization, battery lifespan, and system reliability. This positions the proposed design as a robust and efficient solution for modern hybrid renewable energy systems.