Developing a resilient framework for electric vehicle charging stations harnessing solar energy, standby batteries and grid integration with advanced control mechanisms

Abstract A direct consequence of the rapid expansion of civilization and modernization trends is the escalation in global warming and the consequential climatic upheavals. The world has actively advocated the adoption of electric vehicles (EVs) as a response to the environmental challenges posed by vehicular emissions. It is evident that conventional fuel‐based charging infrastructures are economically impractical and lack organizational cohesion in light of the proliferation of EVs. An EV charging station powered by renewable energy presents a promising opportunity for enhancing flexibility and control. It is imperative that EV charging stations be equipped with solar power and standby batteries (SBBs). Consequently, this article presents and evaluates a system that utilizes a proportional‐integral‐derivative controller, a neural network‐equipped grid and a charging station utilizing a Dragon Fly Optimization Algorithm to generate power and a maximum power point tracking controller. To achieve optimal power management within the charging station, MATLAB/Simulink is used to implement and rigorously test the proposed system. It orchestrates the interaction between the solar panel, backup battery, grid and EVs. Compared to existing systems in the literature, the comprehensive system exhibits commendable efficiency. Due to the pivotal role played by grid integration and the SBB, the system can ensure a reliable power supply to the charging station under any weather conditions.

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Publication Details

Journal
Energy Science & Engineering
Published
2024-09-19
DOI
https://doi.org/10.1002/ese3.1888
Citations
11
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
2.24

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article

Developing a resilient framework for electric vehicle charging stations harnessing solar energy, standby batteries and grid integration with advanced control mechanisms

Chiranjit Sain, Debabrata Mazumdar, Pabitra Kumar Biswas⃰, Luluwah Al‐Fagih et al.
11 citations
Energy Science & Engineering
Advanced Battery Technologies Research
2.24
article

Developing a resilient framework for electric vehicle charging stations harnessing solar energy, standby batteries and grid integration with advanced control mechanisms

Chiranjit Sain, Debabrata Mazumdar, Pabitra Kumar Biswas⃰, Luluwah Al‐Fagih, Furkan Ahmad
article en
11 citations

Abstract

Abstract A direct consequence of the rapid expansion of civilization and modernization trends is the escalation in global warming and the consequential climatic upheavals. The world has actively advocated the adoption of electric vehicles (EVs) as a response to the environmental challenges posed by vehicular emissions. It is evident that conventional fuel‐based charging infrastructures are economically impractical and lack organizational cohesion in light of the proliferation of EVs. An EV charging station powered by renewable energy presents a promising opportunity for enhancing flexibility and control. It is imperative that EV charging stations be equipped with solar power and standby batteries (SBBs). Consequently, this article presents and evaluates a system that utilizes a proportional‐integral‐derivative controller, a neural network‐equipped grid and a charging station utilizing a Dragon Fly Optimization Algorithm to generate power and a maximum power point tracking controller. To achieve optimal power management within the charging station, MATLAB/Simulink is used to implement and rigorously test the proposed system. It orchestrates the interaction between the solar panel, backup battery, grid and EVs. Compared to existing systems in the literature, the comprehensive system exhibits commendable efficiency. Due to the pivotal role played by grid integration and the SBB, the system can ensure a reliable power supply to the charging station under any weather conditions.

Energy Science & EngineeringVol. 12(10)
Hamad bin Khalifa University (QA), Government of Mizoram (IN), Maulana Azad National Institute of Technology (IN), Qatar Foundation (QA)
Qatar National Library
Openalex Percentile: Top 12%
Advanced Battery Technologies Research
2.24
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