Pressure and oxygen excess ratio control of PEMFC air management system based on neural network and prescribed performance

The simultaneous control of the pressure and oxygen excess ratio (OER) plays an important role in improving the performance and safety of polymer electrolyte membrane fuel cell (PEMFC). Nevertheless, the coupling characteristics and nonlinearity existing in the PEMFC air management system need to be solved. To this end, the original coupled nonlinear PEMFC air management system is first transformed into the cathode pressure and OER subsystems by using input–output linearization. For the cathode pressure subsystem, the neural network (NN) control scheme is proposed to maintain the stable tracking of cathode pressure. For the OER subsystem, a prescribed performance function (PPF) is proposed and therefore, the overshoot and steady state of OER tracking error is guaranteed within the quantitative boundary. Moreover, the restriction that the initial error is within the performance function bound is relaxed by proposing a tuning function. Finally, the Lyapunov stability theory, numerical simulations and hardware-in-loop (HIL) experiments show the effectiveness of the proposed controller. Compared with the proportional integral derivative (PID) controller, NN controller without PPF and NN controller with conventional PPF, the NN controller with the proposed PPF can realize the quantitative adjustment of OER and give more improvements to the system safety.