A Model Predictive Control Scheme Without Current Sensor of Dual Active Bridge DC–DC Converters: Improving Dynamic Performance and Reducing Hardware Cost

Dual active bridge dc–dc (DAB) converters have a wide range of applications. However, the stability of DAB converters may be destroyed by the output voltage disturbance, which is caused by variations in input voltage and load conditions. To improve the dynamic performance of DAB converters, many optimization schemes are proposed and require additional voltage and current sensors. In this article, a model predictive control (MPC) method is proposed to achieve fast dynamic performance in load current sensorless conditions, which leads to cost reduction of the hardware system. The proposed MPC method is insensitive to circuit parameters and easy to implement. Moreover, combined with inductor current stress optimization (CSO), the proposed method can achieve multiobject optimization of DAB converters, including improving dynamic response and reducing the cost of the hardware system. First, the transmission power model under dual-phase shift (DPS) of DAB converters is analyzed. Then, some typical methods, such as the proportional–integral (PI) control, the load current feedforward (LCFF) control, and the sliding mode-based direct power control (SM-DPC), are introduced. Finally, an experiment phototype of DAB converters is developed, and comprehensive experiments with resistive load and three-phase inverter load are employed to verify the correctness and effectiveness of the proposed method.