Towards artificial intelligence for solar charge controller: an analytical study of recent status, optimization and module development

The rising global demand for power, allied with the compelling necessity to shift to sustainable energy sources, has heightened attention on renewable energy technologies, notably solar energy. Photovoltaic (PV) systems encounter efficiency challenges from the inherent nonlinearity associated with fluctuating atmospheric conditions. Solar charge controllers (SCC) are vital components in PV systems designed to improve the operational efficiency of solar panels by controlling voltage and current fluctuations. A comprehensive analysis of 100 publications extracted from the Scopus database was performed to assess the evolution and influence of SCC modules in PV applications. The analysis included growth trends, pros and cons, top keywords and topics, document types, authorship evaluations, constraints confronting solar PV systems, and the identified solution. The findings indicate that SCC modules, particularly those employing maximum power point (MPP) tracking techniques, significantly enhance system efficiency. The study emphasizes the potential of artificial intelligence (AI)-driven computer optimization techniques to improve energy efficiency, decrease pollutants, and alleviate greenhouse gas emissions. This research underscores the importance of SCC modules and AI-driven optimization techniques in enhancing energy efficiency and sustainability in renewable energy technologies, offering valuable insights for future advancements in the energy sector.