Optimizing charging battery efficiency in partially shaded PV systems with enhanced particle swarm optimization using
Youness Hakam1,2, Mohamed Tabaa2, Hajar Ahessab1
1Research Laboratory of Physics and Engineers Sciences (LRPSI), Research Team in Embedded Systems, Engineering, Automation, Signal, Telecommunications and Intelligent Materials (ISASTM), Polydisciplinary Faculty (FPBM), Sultan Moulay Slimane University (USMS), Beni Mellal, Morocco.
Science Progress
|April 22, 2025
Summary
This study introduces an intelligent electric vehicle charging system using solar power and an Enhanced Particle Swarm Optimization (E-PSO) algorithm. The system efficiently optimizes solar energy extraction, reducing charging times and improving power output.
Area of Science:
- Electrical Engineering
- Renewable Energy Systems
- Artificial Intelligence
Background:
- Electric vehicle (EV) charging stations require efficient power management, especially when integrating renewable sources like photovoltaic (PV) systems.
- Solar irradiation variability impacts PV output, necessitating intelligent optimization strategies for consistent power delivery.
- Traditional Maximum Power Point Tracking (MPPT) techniques may struggle with dynamic environmental conditions.
Purpose of the Study:
- To develop an intelligent battery charging system for EVs that integrates a PV system with a buck converter.
- To enhance power transfer efficiency in partial shade conditions using an optimization algorithm.
- To improve the overall performance and reduce charging duration for electric vehicles.
Main Methods:
- Implementation of a buck converter with a DSP F28379D microcontroller for high-frequency pulse-width modulation (PWM) signal generation.
- Utilization of an Enhanced Particle Swarm Optimization (E-PSO) algorithm for optimizing power extraction from the PV system.
- Experimental validation of the proposed system under varying operating conditions.
Main Results:
- The E-PSO algorithm demonstrated a rapid response time of 0.04 seconds and high efficacy of 99.90%.
- Significant improvements in power output and a marked decrease in charging duration compared to traditional MPPT methods.
- Effective power regulation and enhanced efficiency, particularly in partial shade scenarios.
Conclusions:
- The proposed intelligent charging system, leveraging E-PSO, is effective and feasible for adaptive EV charging.
- The system addresses the challenges of solar irradiation unpredictability for reliable EV charging infrastructure.
- This approach offers a promising solution for optimizing renewable energy utilization in electric vehicle charging stations.
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