Open AccessModel-based predictive control of dc-dc converter for EV applications
Author(s) -
ChangHyun Kim,
Houng Kun Joung
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.12.11312
Subject(s) - converters , control theory (sociology) , controller (irrigation) , model predictive control , electric vehicle , power (physics) , computer science , optimal control , control system , sampling (signal processing) , electric power system , control (management) , control engineering , engineering , voltage , electrical engineering , mathematics , mathematical optimization , agronomy , physics , filter (signal processing) , quantum mechanics , artificial intelligence , biology
Background/Objectives: The power performance of electric vehicle chargers depends on the control efficiency of the power converters with on-board and off-board types. In this paper, a new control method is proposed for power converter of fast electric vehicle chargers in order to improve the power efficiency.Methods/Statistical analysis: The proposed control method is the optimal control to minimize the performance objectives from the predicted output, based on the system model. The discretized model of DC-DC converter with sampling time is derived by using lifting operation for taking into account with the desired prediction time.Findings: The existing conventional controllers are obtained by off-line optimal solution and applied to the systems. Once the control gain is determined, the controller is able to reflect the system response at the real-time.Improvements/Applications: The proposed control method has advantages to deal with system performances at real-time and the control actuation is updated every sampling time via the derived mathematical model. It can be directly applicable to real electric vehicle charger systems in industry.