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

January 2025
Energy Systems

DOI:10.1007/s12667-024-00721-4

Authors:

Akmal Zaini Arsad

Universiti Tenaga Nasional

Siti Rosilah Arsad

Sunway University

Ahmad Wafi Mahmood Zuhdi

Universiti Tenaga Nasional

Show all 6 authorsHide

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.

Schematic representation of the a review's selection procedure and b overall review results

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Comparison of the number of papers before and after scanning

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The sources of carbon dioxide by various industries [47]

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Advantages and disadvantages of PWM and MPPT controllers [52]

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a Co-occurrence keywords of SCC module using VOS viewer, b Sankey diagram for SCC module keyword evaluation, c top 18 keywords for 100 citation articles, and d top 14 SCC module topics for 100 citation articles

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Vol.:(0123456789)

Energy Systems

https://doi.org/10.1007/s12667-024-00721-4

REVIEW ARTICLE

Towards artiﬁcial intelligence forsolar charge controller:

ananalytical study ofrecent status, optimization

andmodule development

A.Z.Arsad1 · A.D.Azhar2· S.R.Arsad3· A.W.M.Zuhdi1,2· C.F.Chau1,2·

A.Ghazali2

Received: 10 August 2024 / Accepted: 12 November 2024

Abstract

The rising global demand for power, allied with the compelling necessity to shift

to sustainable energy sources, has heightened attention on renewable energy tech-

nologies, notably solar energy. Photovoltaic (PV) systems encounter eﬃciency chal-

lenges from the inherent nonlinearity associated with ﬂuctuating atmospheric condi-

tions. Solar charge controllers (SCC) are vital components in PV systems designed

to improve the operational eﬃciency of solar panels by controlling voltage and cur-

rent ﬂuctuations. A comprehensive analysis of 100 publications extracted from the

Scopus database was performed to assess the evolution and inﬂuence of SCC mod-

ules in PV applications. The analysis included growth trends, pros and cons, top

keywords and topics, document types, authorship evaluations, constraints confront-

ing solar PV systems, and the identiﬁed solution. The ﬁndings indicate that SCC

modules, particularly those employing maximum power point (MPP) tracking tech-

niques, 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.

Keywords Artiﬁcial intelligence· Solar energy· Solar charge controller· Highly

cited articles· Bibliometric analysis· Solar PV emerging technology

* A. Z. Arsad

akmalzaini@uniten.edu.my

1 Institute ofSustainable Energy, Universiti Tenaga Nasional (UNITEN), 43000Kajang,

Selangor, Malaysia

2 College ofEngineering, Universiti Tenaga Nasional (UNITEN), 43000Kajang, Selangor,

Malaysia

3 Department ofEngineering, School ofEngineering andTechnology, Sunway University, Bandar

Sunway, 47500PetalingJaya, Malaysia

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

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