Volume 53

Model Predictive Control of PEMFC Air Supply System Based on Kalman State Estimation of Unmeasurable Parameters Haowen Shen, Fengxiang Chen

Abstract

The air supply subsystem is critical for proton exchange membrane fuel cells, determining the output performance of the stack. By controlling the speed of the air compressor and the opening of the back pressure valve, it is possible to achieve appropriate regulation of air supply pressure and mass flow rate. However, this process presents challenges due to disturbances and the coupling of multiple variables. This study employs the model predictive control algorithm to address these issues because of its strong decoupling capabilities and robustness. Initially, an M-sequence is designed to identify the system and obtain the predictive model for the MPC. Based on feedback output, a Kalman filter is then used to estimate the optimal unmeasurable state information. Subsequently, the MPC controller is designed to obtain the optimal control output under various constraints. Finally, by using a traditional PID controller as a control group, the performance of the proposed MPC controller based on Kalman state estimation is analyzed under current step change conditions.

Keywords fuel cell, model predictive control, Kalman estimation