A multivariable sliding mode predictive control method for the air management system of automotive fuel cells

The proton exchange membrane fuel cell gas control has been one key point in fuel cell management systems. The complexity and coupling of the air management system make it difficult to achieve precise air intake adjustment. In this paper, an accurate joint control method for the air flow and pressure regulation is proposed. The nonlinear mathematical model of the air management system is developed to describe the output characteristic and state change. Based on this, the feedback linearization method is proposed to obtain the direct correspondence between control variables and controlled variables. In addition, to solve the problem that the controlled variables cannot be measured directly, an extended state observer is applied to estimate the stack cathode pressure. The sliding mode predictive control method is proposed to control the oxygen excess ratio and cathode pressure simultaneously. The relative order of the system is used to design the sliding mode surface, and the corresponding predictive model is proposed. The results obtained by simulation experiments show that pressure and mass flow have little effect on each other through decoupling. The proposed algorithm has been verified to have high precision, fast response, and robustness through comparative experiments