State-Set-Optimized Finite Control Set Model Predictive Control for Three-Level Non-Inverting Buck–Boost Converters

Three-level non-inverting buck–boost converters are promising for electric vehicle charging stations due to their wide voltage regulation capability and bidirectional power flow. However, the number of three-level operating states is four times that of two-level operating states, and the lack of a unified switching state selection mechanism leads to serious challenges in its application. To address these issues, a finite control set model predictive control (FCS-MPC) strategy is proposed, which can determine the optimal set and select the best switching state from the excessive number of states. Not only does the proposed method achieve fast regulation over a wide voltage range, but it also maintains the input- and output-side capacitor voltage balance simultaneously. A further key advantage is that the number of switching actions in adjacent cycles is minimized. Finally, a hardware-in-the-loop experimental platform is built, and the proposed control method can realize smooth transitions between multiple operation modes without the need for detecting modes. In addition, the state polling range and the number of switching actions are superior to conventional predictive control, which provides an effective solution for high-performance multilevel converter control in energy systems.