Prototyping of solar power charging station using artificial neural network based MPPT algorithm

The reasons for pollution and the limitations of fossil fuels have driven the use of electric vehicles. Electric vehicles are synonymous with electric batteries that require periodic charging. There has been an increase in research on battery charging for electric vehicles. There is also the use of...

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Bibliographic Details
Published in:AIP Conference Proceedings 2024-05, Vol.3116 (1)
Main Authors: Rif’an, Muhammad, Syufrijal, Taryudi, Subekti, Massus, Sabrina, Aulia, Lestari, Siska
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Battery chargers
Electric vehicle charging
Energy harvesting
Energy sources
Photovoltaic cells
Prototyping
Solar energy
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Summary:The reasons for pollution and the limitations of fossil fuels have driven the use of electric vehicles. Electric vehicles are synonymous with electric batteries that require periodic charging. There has been an increase in research on battery charging for electric vehicles. There is also the use of renewable energy resources such as photovoltaics for battery charging stations. This paper focuses on prototyping battery charging stations from solar photovoltaic energy using ANN-based MPPT controllers within the framework of optimizing solar energy harvesting and comparing them with conventional MPPT methods. Test results showed that the prototype of the proposed charging system took 33.3 minutes to charge a 7Ah battery. Compared to conventional MPPT methods, the charging time of MPTT methods with Neural Network is 6.01% faster.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0210192