Active disturbance rejection control strategy for PEMFC oxygen excess ratio based on adaptive internal state estimation using unscented Kalman filter

The effectiveness of gas flow control will significantly affect the output performance of the PEMFC, however, the gas state inside the cathode is difficult to observe due to the tightness and safety requirements. To achieve effective control of PEMFC in the presence of parameter bias, an improved active disturbance rejection control (IADRC) strategy based on the adaptive unscented Kalman filter (AUKF) is proposed in this paper. A dynamic model of the air supply system with seven state variables was developed, capable of describing the non-linear characteristics of the system, and a semi-empirical air compressor model was designed to match the actual operating characteristics based on the experimental data set. Based on the standard UKF algorithm, an adaptive process was designed using the moving window method to dynamically adjust the noise characteristics to reduce the state estimation bias caused by changes in system structural parameters during service. Finally, the algorithmic structure of the ADRC was optimized to achieve an accurate estimation of the total system perturbation using cascaded extended state observers (ESO), thus achieving a control effect with the advantages of less overshoot and response speed based on fewer tuning parameters. Simulation results show that the proposed strategy can effectively improve the system response and robustness to structural parameters, and has great control effects under real road conditions.

Automated summary

This paper proposes an improved active disturbance rejection control (IADRC) strategy combined with adaptive unscented Kalman filter (AUKF) for effective control of Proton Exchange Membrane Fuel Cell (PEMFC). The strategy is capable of improving system response and robustness to structural parameters, and shows great control effects under real road conditions.