XGBoost based PSO for DAB DC-DC converter triple phase shift modulation

Dual active bridge (DAB) converter is an important converter for electric vehicles, energy storage systems. Data sets are introduced to control the DC-DC power control. Triple phase-shift of DAB is controlled by the range of zero voltage switching. Optimal switching to reduce high current stress DC-DC converter is In presented data sets are used with python program to execute the different stages of machine learning. Performance of all stages are well addressed each data output file. Multiple phase shift angles are used for a triple phase-shift control algorithm to achieve different operational modes.  For optimal switching duty cycles D0, D1 and D2, output of machine learning is trained and tested. Test results are fed to PSO to obtain the optimal switching duty cycles. The proposed extreme gradient boosting-based PSO triple phase-shift modulation, XGB −PSO−TPSM is trained and tested. Performance of proposed XGB −PSO−TPSM is exhibited by a many data sets for achieving reduced converter current stress. Better power control is assured by the XGB −PSO−TPSM is trained data in renewable energy system. Electric vehicle is one the best suitable and demanded application of DAB converter to ensure proper power control.