Current-Sensorless Model Predictive Control of Dual Active Bridge Converters with Kalman Filter

Abstract

Dual active bridge (DAB) converter has become the critical equipment in fuel cell vehicle (FCV) electrical systems. For FCV electrical systems, the DAB converter needs to obtain constant output voltage rapidly and accurately under all working conditions. Model predictive control (MPC), well-known for its fast dynamics, is a particularly promising strategy to achieve this. However, MPC relies on accurate system model, parameters mis-match will affect the control performance considerably. Besides, additional current sensor is also introduced when using MPC method, which increases the hardware cost. To tackle this issue, in this work, we propose a current-sensorless MPC method based on Kalman filter. The current estimated by the Kalman filter is used instead of the sampling one. Compared with traditional MPC, the proposed method depends little on the accurate model parameters. Simulation data verify that, the proposed control strategy (i) is able to reduce the current sensor, (ii) has strong robustness, (iii) does not sacrifice control dynamic performance.