Smart Extreme Fast Portable Charger for Electric Vehicles-Based Artificial Intelligence

Mahdi Mosayebi, Meysam Gheisarnejad Chirani, Hamed Farsizadeh, Björn Andresen, Mohammad Hassan Khooban

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Abstract

Due to the increase in the number of electric vehicles (EVs) in the world, there is a need to design superior performance and lower operating costs of charging infrastructures to serve these vehicles. Merging extreme fast charging technology with the portable feature can provide a user-friendly and cost-effective structure for charging EVs. This brief proposes a Smart Extreme Fast Portable Charger (SEFPC) for Electric Vehicles which have several input ports (e.g., the power grid or Renewable Energy Sources (RESs)/Energy Storage Systems (ESSs)) and an output port with an optimal charging operation mode based on considering the condition of the available power sources and battery of EVs for saving the energy and time charging. Moreover, a model-free sliding mode controller-based machine learning algorithm is applied to find optimal charging operation mode based on the battery state and power of sources condition to increase battery life and overall system efficiency. Finally, real-time results based on the OPAL-RT are provided to validate the efficacy and feasibility of the proposed SEFPC and model-free sliding mode controller.

OriginalsprogEngelsk
TidsskriftIEEE Transactions on Circuits and Systems II: Express Briefs
Vol/bind70
Nummer2
Sider (fra-til)586-590
Antal sider5
ISSN1549-7747
DOI
StatusUdgivet - feb. 2023

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