Application of adaptive stepped PID control with in hydrogen fuel system

Ge Lixia; Mohammad Nizamuddin Inamdar; Aiman Alodaini; Chen Chen

doi:10.31893/multiscience.2025434

Keywords:

hydrogen fuel cell

hydrogen pressure control

simulink

Abstract

Hydrogen fuel cells are increasingly being applied in the field of new energy, and corresponding control systems have also been extensively studied. Overshoot is easy to occur during the control of hydrogen inlet pressure in fuel cell system, especially under the special conditions such as shutdown air supplementation and pressure maintenance leak detection. The excessive hydrogen pressure or pressure difference caused by overshoot may cause damage to the fuel cell stack.The hydrogen supply module control in the hydrogen circuit has the characteristics of nonlinear and complex mathematical model, and it is difficult to obtain better control effect with traditional PID control. This paper proposes an adaptive stepping PID hydrogen pressure control method, This paper establishes the pressure flow model of hydrogen circuit, and establishes the algorithm model of adaptive stepping PID and traditional PID in MATLAB/Simulink.The simulation results and experimental comparison prove that the adaptive stepping PID control has excellent control effects on complex systems with large lag, time-variant, and nonlinear characteristics, featuring fast response and the ability to suppress hydrogen pressure overshoot,which can improve the pressure control accuracy of the hydrogen circuit in the hydrogen fuel cell system with significant engineering significance and practical impact.
References
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2. Chen, F. X., Yu, Y., & Liu, Y. (2017). Control system design for proton exchange membrane fuel cell based on a common rail (II): optimization and schedule scheme for the common rail. International Journal of Hydrogen Energy, 42(7): 4294-4301.
3. Chen, F. X., Yu, Y., Liu, Y., et al. (2017). Control system design for proton exchange membrane fuel cell based on a common rail (I): Control strategy and performance analysis. International Journal of Hydrogen Energy, 42(7), 4285-4293.
4. Deng, H. W., Li, Q., Chen, W. R., et al. (2018). High-order sliding mode observer based OER control for PEM fuel cell air-feed system. IEEE Transactions on Energy Conversion, 33(1), 232-244.
5. Deng, Y. X., & Liu, J. (2024).Hydrogen leakage pressure control strategy for hydrogen fuel cell vehicles.Automobile and New Powertrain, 7(3),32-35.
6. Ding, T. W., Huang, X., Wang, Z. P., et al. (2019). Research on optimal gas supply pressure of fuel cell system based on data drive. In Proceedings of the 2019 China Society of Automotive Engineers (p.481-486). Shanghai, China.
7. Dong, Y., & Si, W. K. (2019). Research on starting control system of DC motor based on stepping PID control. Journal of Tonghua Normal University, 40(287), 12–15.
8. Hong, Lin. (2017). Fuel Delivery Control for Vehicular Fuel Cell Power Systems. Zhejiang University, Hangzhou, China.
9. Lian, J., Fang, S. Y., & Zhou, Y. F. (2020). Control of fuel cell cathode system based on state quantity estimation. Computer Integrated Manufacturing Systems, 37(7), 119-122.
10. Liu, J. K. (2023).Advanced PID Control MATLAB Simulation. Electronic Industry Press.Beijing,China.
11. Liu, Z. Y., Chen, J., Liu, H., et al. (2020). Anode purge management for hydrogen utilization and stack durability improvement of PEM fuel cell systems. Applied Energy, 275(0),115110.
12. Lu, F., Wang, S. K., Zhu, H., et al. (2018). Development of Power System for a Hydrogen Fuel Cell Bus. Bus & Coach Technology and Research, 40(4), 1-3.
13. Nanadegani, F. S., Lay, E. N., Iranzo, A., et al. (2020). On neural network modeling to maximize the power output of PEMFCs. Electrochimica Acta, 348(0), 136345.
14. Nie, W., Sun, Z., Zhou, B. J., et al. (2021). Research on hydrogen pressure control strategy of fuel cell power system. Marine Electric & Electronic Engineering, 41(06), 45-50.
15. Pukreshpan, J. T., Stefanopoulou, A. G., & Peng, H. (2004). Control of fuel cell breathing. lEEE Control Systems,24(2), 30-46.
16. Pukrushpan, T., Stefanopoulou, A. G., & Peng, H. (2004). Control of Fuel Cell Power Systems: Principles, Modeling, Analysis, and Feedback Design (2nd). Springer, London.
17. Qin, B., Wang,X. L., Wang, L., et al. (2023). Adaptive slide mode control of gas feeding of proton exchange membrane fuel cell system. Control Theory & Applications, 40(11), 2049-2058.
18. Qiu, J. Zh., Zhao, H., Mou, L., & others. (2022). Temperature control of proton exchange membrane fuel cell based on particle swarm optimization PID. Manufacturing Automation, 44(8), 98–101.
19. Song, K., Zhang, T., Niu, W. X., et al. (2017). Error accumulation problem and solution of dynamic programming algorithm for energy management of fuel cell electric vehicles. Automotive Engineering, 39(3), 249-255.
20. Wu, Y. H., &Lin, Y. F. (2021). Adaptive control of hydrogen pressure of fuel cell. Electric Power System Equipment, (6), 157-158.
21. Xue, G. Q., Yuan, Y. P., Li, N., & others. (2024). Feed-forward PID cooperative control of air flow and pressure in PEMFC system. Journal of Wuhan University of Technology, 46(9), 127–135.
22. Zhang, K. J., Qu, D. W., Lan, H. X., et al. (2021). Hydrogen fuel cell system modeling and simulation based on MATLAB / Simulink. Science Technology and Engineering, 21(13), 5380-5386.
23. Zhang, X. J. (2019). Safety analysis of hydrogen system on fuel cell vehicles. Auto Time, (2), 98-100.
24. Zheng, L., Hou, Y., Zhang, T., et al. (2021). Performance prediction of fuel cells using long short-term memory recurrent neural network. International Journal of Energy Research, 45(6), 9141-9161.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Lixia, G., Inamdar, M. N., Alodaini, A., & Chen, C. (2025). Application of adaptive stepped PID control with in hydrogen fuel system. Multidisciplinary Science Journal, 7(9), 2025434. https://doi.org/10.31893/multiscience.2025434

[1] Chang, J. J., Wang, X. L., Fang, J. P., & others. (2021). Study on control strategy for anode and cathode pressures in proton exchange membrane fuel cell. Automotive Engineering, 43(10), 1466–1471.

[2] Chen, F. X., Yu, Y., & Liu, Y. (2017). Control system design for proton exchange membrane fuel cell based on a common rail (II): optimization and schedule scheme for the common rail. International Journal of Hydrogen Energy, 42(7): 4294-4301.

[3] Chen, F. X., Yu, Y., Liu, Y., et al. (2017). Control system design for proton exchange membrane fuel cell based on a common rail (I): Control strategy and performance analysis. International Journal of Hydrogen Energy, 42(7), 4285-4293.

[4] Deng, H. W., Li, Q., Chen, W. R., et al. (2018). High-order sliding mode observer based OER control for PEM fuel cell air-feed system. IEEE Transactions on Energy Conversion, 33(1), 232-244.

[5] Deng, Y. X., & Liu, J. (2024).Hydrogen leakage pressure control strategy for hydrogen fuel cell vehicles.Automobile and New Powertrain, 7(3),32-35.

[6] Ding, T. W., Huang, X., Wang, Z. P., et al. (2019). Research on optimal gas supply pressure of fuel cell system based on data drive. In Proceedings of the 2019 China Society of Automotive Engineers (p.481-486). Shanghai, China.

[7] Dong, Y., & Si, W. K. (2019). Research on starting control system of DC motor based on stepping PID control. Journal of Tonghua Normal University, 40(287), 12–15.

[8] Hong, Lin. (2017). Fuel Delivery Control for Vehicular Fuel Cell Power Systems. Zhejiang University, Hangzhou, China.

[9] Lian, J., Fang, S. Y., & Zhou, Y. F. (2020). Control of fuel cell cathode system based on state quantity estimation. Computer Integrated Manufacturing Systems, 37(7), 119-122.

[10] Liu, J. K. (2023).Advanced PID Control MATLAB Simulation. Electronic Industry Press.Beijing,China.

[11] Liu, Z. Y., Chen, J., Liu, H., et al. (2020). Anode purge management for hydrogen utilization and stack durability improvement of PEM fuel cell systems. Applied Energy, 275(0),115110.

[12] Lu, F., Wang, S. K., Zhu, H., et al. (2018). Development of Power System for a Hydrogen Fuel Cell Bus. Bus & Coach Technology and Research, 40(4), 1-3.

[13] Nanadegani, F. S., Lay, E. N., Iranzo, A., et al. (2020). On neural network modeling to maximize the power output of PEMFCs. Electrochimica Acta, 348(0), 136345.

[14] Nie, W., Sun, Z., Zhou, B. J., et al. (2021). Research on hydrogen pressure control strategy of fuel cell power system. Marine Electric & Electronic Engineering, 41(06), 45-50.

[15] Pukreshpan, J. T., Stefanopoulou, A. G., & Peng, H. (2004). Control of fuel cell breathing. lEEE Control Systems,24(2), 30-46.

[16] Pukrushpan, T., Stefanopoulou, A. G., & Peng, H. (2004). Control of Fuel Cell Power Systems: Principles, Modeling, Analysis, and Feedback Design (2nd). Springer, London.

[17] Qin, B., Wang,X. L., Wang, L., et al. (2023). Adaptive slide mode control of gas feeding of proton exchange membrane fuel cell system. Control Theory & Applications, 40(11), 2049-2058.

[18] Qiu, J. Zh., Zhao, H., Mou, L., & others. (2022). Temperature control of proton exchange membrane fuel cell based on particle swarm optimization PID. Manufacturing Automation, 44(8), 98–101.

[19] Song, K., Zhang, T., Niu, W. X., et al. (2017). Error accumulation problem and solution of dynamic programming algorithm for energy management of fuel cell electric vehicles. Automotive Engineering, 39(3), 249-255.

[20] Wu, Y. H., &Lin, Y. F. (2021). Adaptive control of hydrogen pressure of fuel cell. Electric Power System Equipment, (6), 157-158.

[21] Xue, G. Q., Yuan, Y. P., Li, N., & others. (2024). Feed-forward PID cooperative control of air flow and pressure in PEMFC system. Journal of Wuhan University of Technology, 46(9), 127–135.

[22] Zhang, K. J., Qu, D. W., Lan, H. X., et al. (2021). Hydrogen fuel cell system modeling and simulation based on MATLAB / Simulink. Science Technology and Engineering, 21(13), 5380-5386.

[23] Zhang, X. J. (2019). Safety analysis of hydrogen system on fuel cell vehicles. Auto Time, (2), 98-100.

[24] Zheng, L., Hou, Y., Zhang, T., et al. (2021). Performance prediction of fuel cells using long short-term memory recurrent neural network. International Journal of Energy Research, 45(6), 9141-9161.

Abstract

References

How to cite