Dual active bridge DC-DC converter design and optimization-based control using metaheuristic algorithms

In this thesis study, a comprehensive modeling, analysis, and optimization process was carried out to improve the control performance of a double-active bridge (DAB) DC-DC converter. Today, DAB converters play an important role in the fast-charging infrastructure of electric vehicles (EVs) due to their advantages such as bidirectional energy transfer and isolation. In this context, improving the control performance of DAB converters makes EV systems more reliable and efficient. Within the scope of the study, the switching, average, and small signal models of the DAB converter were analyzed. Within the scope of the study, proportional-integral (PI) and fractional-order proportional-integral (FOPI) controllers were used to control the DAB converter, and the parameters of these controllers were optimized using ten different metaheuristic algorithms. During the optimization process, the Integrated Time-Weighted Absolute Error (ITAE) and Zwe-Lee Gaing (ZLG) objective functions were chosen as performance criteria. The results were evaluated comparatively to reveal the effects of the algorithms on the control performance of the DAB converter. This thesis presents a comprehensive approach to determining the control parameters of PI and FOPI controllers in the control of DAB converters using different metaheuristic algorithms, thereby making significant contributions to the literature.