In order to achieve the goal of carbon neutralization, hydrogen plays an important role in the new global energy pattern, and its development has also promoted the research of hydrogen fuel cell vehicles. The air supply system is an important subsystem of hydrogen fuel cell engine. The increase of air supply can improve the output characteristics of a fuel cell, but excessive gas supply will destroy the pressure balance of the anode and cathode. In the actual operation of a proton-exchange membrane fuel cell, considering the load change, it is necessary not only to ensure the stability of reactor pressure but also to meet the rapid response of inlet pressure and flow in the process of change. Therefore, the coordinated control of the two is the key to improving fuel cell output performance. In this paper, the dynamic model of the intake system is built based on the mechanism and experimental data. On this basis, the double closed-loop proportion integration differentiation (PID) control and feedforward compensation decoupling PID control are carried out for the air supply system, respectively. Then, the fuzzy neural network decoupling control strategy is proposed to make up for the shortcomings that the double closed-loop PID cannot achieve decoupling and the feedforward compensation decoupling does not have adaptability. The results show that the fuzzy neural network control can realize the decoupling between air intake flow and pressure and ensure that the air intake flow and pressure have a good follow-up, and the system’s response speed is fast.

To ensure the energy conversion efficiency of a proton-exchange membrane fuel cell (PEMFC), it is necessary to establish a water-cooled cooling system to keep the inlet temperature of fuel-cell coolant and the temperature difference between the inlet and outlet temperature within the set range. First, a semi-empirical and semi-mechanism model was built in Simulink. Then, a variable-universe fuzzy PID controller was designed to adjust the quantization factor and scaling factor by scaling factor α1, α2 and β to improve the accuracy of the control results. Finally, the cooling system model with controller is simulated. The results show that compared with other control methods, the simulation results of the variable-universe fuzzy PID controller have smaller maximum overshoot, faster response speed and more gentle temperature fluctuation.

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