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.
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Circuits and Systems II: Express Briefs |
| Volume | 70 |
| Issue | 2 |
| Pages (from-to) | 586-590 |
| Number of pages | 5 |
| ISSN | 1549-7747 |
| DOIs | |
| Publication status | Published - Feb 2023 |
Keywords
- Electric vehicles
- charger
- fast charger
- machine learning
- sliding mode control