Method for system parameter identification and controller parameter tuning for super-twisting sliding mode control in proton exchange membrane fuel cell system

Abstract

Abstract The super-twisting sliding mode control (ST-SMC) is widely used in fuel cell system control due to the simple control law and strong robustness. However, it requires an accurate system model. Generally, literature usually reduces the system order and directly gives the empirical value for the system and controller parameters or conducts coefficient identification of the fuel cell voltage model, lacking the specific identification method for system physical parameters and controller parameters. In this paper, a relatively complete control-oriented nine-state fuel cell system model was established, including the model of compressor flow using the artificial neural network method, and the improved voltage model. Then, the data-driven method for key parameter identification was proposed, including the fuel cell throttle factor and motor voltage changing rate considering time delay. In addition, the parameter tuning method for controller design was proposed as well. These two methods are of originality. After the model validation in the perspective of steady and transient performance, the comparison was carried out between the ST-SMC and PID controller. It is found that the throttle factor of the cathodic fuel cell inlet and the delay effect in terms of changing rate of motor voltage impact the system model, where the throttle factor is time-variant, and the delay is noticeable which differs with the step magnitude of the motor voltage. The parameter tuning and boundary estimation of ST-SMC are very specific, owing to the process of treating flow rate as a state, not speed, and are convenient to be generalized. The better ability in anti-flooding exhibits the importance of parameter identification. Although the study is conducted in a low-pressure system, the method proposed in this paper is universal and could be applied to other fuel cell controls for better system efficiency and reliability.