Anti-disturbance control of oxygen feeding for vehicular fuel cell driven by feedback linearization model predictive control-based cascade scheme

The accurate control of automotive fuel cell oxygen excess ratio (OER) is necessary to improve system efficiency and service life. To this end, an anti-disturbance control driven by a feedback linearization model predictive control (MPC)-based cascade scheme is proposed. It considers strong nonlinear coupling and disturbance injection of fuel cell oxygen supply. A six-order nonlinear fuel cell oxygen feeding model is presented. It is further formulated using an extended state observer to rapidly reconstruct the OER, to overcome the slow response and interference errors of sensor measurements. In the proposed cascade control, the outer loop is the anti-disturbance control which is used to realize the optimized OER tracking and the inner loop via the feedback linearization to linearize the oxygen feeding behaviors conducts MPC to regulate the air compressor output mass flow. The feedback linearization demonstrates a robust tracking performance of nonlinear outputs, and the integral absolute error of anti-disturbance control is 0.3021 lower than that of PI control under a custom test condition. Finally, the numerical validation on a hybrid driving cycle indicates that the proposed cascade control can regulate the fuel cell OER with an average absolute error of 0.02313 in the high air compressor operation efficiency zone.