Model Predictive Control With Power Self-Balancing of the Output Parallel DAB DC–DC Converters in Power Electronic Traction Transformer

Abstract

This paper focuses on the output parallel dual-active-bridge (DAB) dc–dc converters in power electronic traction transformers. A model predictive control with current-stress-optimized (MPC-CSO) scheme based on dual-phase-shift (DPS) control is proposed to improve the dynamic performance, balance the transmission power, and realize the current-stress optimization. The dynamic behavior of output voltage in the next horizon is predicted accurately under the input voltage fluctuation and load disturbance conditions by developing the prediction model. In addition, the proposed MPC-CSO scheme can track the output voltage with the desired value directly with no overshoot during the start-up process. Combining the MPC and CSO scheme, the fast dynamic response and high-efficiency performance of DAB converters can be achieved simultaneously, and the transmission power of each DAB cell can be self-balanced. Finally, three control schemes consisting of traditional voltage closed-loop control with single phase shift, traditional CSO control with DPS, and MPC-CSO with DPS schemes are compared in a scale-down three DAB dc–dc converter cells experimental prototype by using TMS320F28335+FPGA_6SLX45 as core controller. Extensive experimental results have verified the excellent performance of the proposed MPC-CSO scheme and associated analysis in this paper.