mgr Mykola Lukianov
Zatrudnienie
- Asystent w Katedra Energoelektroniki i Maszyn Elektrycznych
- Doktorant w Politechnika Gdańska
Słowa kluczowe Pomoc
- bidirectional ev charger , multi-active bridge dc-dc converter
- bidirectional ev charging
- dc-dc power converters , electric vehicle charging
- dc/dc converters
- energy management, dc traction systems, dc microgrids, energy storage systems, model predictive control, demand response program
- ev charger
- lv dc traction grid ,
- photovoltaic systems , renewable energy sources , accuracy , machine learning algorithms , machine learning , artificial neural networks , predictive models
- power converter interface
- renewable energy sources
Media społecznościowe
Kontakt
- mykola.lukianov@pg.edu.pl
Wybrane publikacje
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An Overview of Bidirectional EV Chargers: Empowering Traction Grid-Powered Chargers
In recent years, the number of electric vehicles has been at least doubling year after year. As a result, today electric vehicles already account for approximately 10% of the global automotive market, which positively affects environment in urbanized areas. However, to take full advantage of the EV integration it is necessary to use renewable sources for their charging; optimally place charging stations/terminals; optimally manage...
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Power converter interface for urban DC traction substations - solutions and functionality.
This paper focuses on extending an urban DC traction substation functionality by means of an additional power converter interface (PCI). In particular, by enabling bidirectional energy exchange between LV DC traction grid, AC grid and V2G chargers. Among other things, the presented material compares general attributes of the most promising DC/DC converters that can be used in a PCI, meet the requirements of galvanic isolation and...
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Advancing Solar Energy: Machine Learning Approaches for Predicting Photovoltaic Power Output
This research is primarily concentrated on predicting the output of photovoitaic power, an essential field in the study of renewable energy. The paper comprehensively reviews various forecasting methodologies, transitioning from conventional physical and statistical methods to advanced machine learning (ML) techniques. A significant shift has been observed from traditional point forecasting to machine learning-based forecasting...
wyświetlono 511 razy