# Sliding Mode Integral Separation PID Control for Hydrogen Fuel Cell Systems
## 1 Introduction
[PERSON] [9] proposed an advanced dynamic model predictive control (AMPC) based on a nonlinear model predictive control (NMPC) framework with a multi\(-\)objective cost function driven by dynamic weights to improve the energy performance of fuel cell hybrid vehicles. [PERSON] [10] proposed a switched auto\(-\)regressive neural control (S\(-\)ANC) that combines switched model predictive control (S\(-\)MPC) with auto\(-\)regressive (AR) control. The novel S\(-\)ANC algorithm can satisfy the constraints with a performance comparable to that of MPC, and, at the same time, it can realize continuous learning, which realizes the efficient use of energy resources. [PERSON] [11] proposed a hybrid method based on \(\varepsilon-\)variables and classical MPC for constructing the S\(-\)MPC for flexible hybrid microgrids with plug\(-\)and\(-\)play (PnP) capabilities. In the study of control strategies for air supply systems, [PERSON] [12] combined fuzzy control and neural networks with PID to control OER. The results showed that the OER settling time was 0.35 s under PID control and 0.16 s under improved PID control. To increase the resilience of PEMFC, [PERSON] [13] proposed a robust H\({}_{\infty}\) controller based on a convex optimization approach. [PERSON] [14] used a super\(-\)twisted sliding mode control (SSMC) and an adaptive dynamic planning\(-\)based controller to ensure the smooth response of the OER control method. Using a fractional\(-\)order PID control technique, [PERSON] [15] monitored the desired excess ratio and air pressure. The findings indicated that changes in system variables had an impact on the control effect. In the study of temperature control strategies for hydrothermal management systems, [PERSON] [16] proposed fuzzy PID and PID control strategies to control the electronic thermostat in a PEMFC system to reduce water temperature fluctuations. The results show that the maximum temperature fluctuation is 2 \({}^{\circ}\)C under PID control and 0.5 \({}^{\circ}\)C under fuzzy PID control. [PERSON] [17] proposed an adaptive sliding mode control (ASMC) based on an extended state observer for the cooling system of a proton\(-\)exchange membrane fuel cell with various disturbances, and the results show that the proposed ASMC can significantly suppress the chattering phenomenon. The control strategies in the above\(-\)mentioned literature [9; 10; 11; 12; 13; 14; 15; 16; 17], with their complicated controller architecture, numerous calculations, and single control variable, are unable to suggest a control strategy that works for the entire PEMFC. In order to lessen system overshoot, increase control accuracy, and guarantee stable system operation, this paper investigates the control method to regulate OER, electric stack temperature, and cathode-anode differential pressure, and subsequently then suggests a sliding mode integral separation PID control strategy. A 3 kW water\(-\)cooled PEMFC experimental system was built using the self\(-\)developed fuel cell controller, and it is confirmed that this control method has a more robust and dynamic response. The main contents of this paper are organized as follows: Section 2 introduces the components of the hydrogen fuel cell system and establishes a simple fuel cell model. Section 3 proposes a sliding mode integral separation PID (SMC\(-\)IS\(-\)PID) control strategy and describes the design process of each part in detail. Section 4 introduces the 3 kW hydrogen fuel cell experimental system constructed by the team, verifies the effectiveness of the model and the proposed control strategy on MATLAB/Simulink 2019 and the experimental system, and analyzes the results. Finally, Section 5 summarizes the research results and future perspectives.