Abstract
This paper proposes a Feedback Linearization-based Adaptive Sliding Mode Controller (FLC-ASMC) to address the coordinated control of gas flow and pressure in the anode and cathode of automotive Proton Exchange Membrane Fuel Cell (PEMFC) systems. Considering the nonlinear and strongly coupled characteristics of PEMFC systems, feedback linearization is employed to decouple the relationship between flow and pressure. The adaptive sliding mode control technique is then integrated to adjust control parameters in real time, ensuring that the anode-cathode pressure difference remains within a reasonable range. The research results demonstrate that the accuracy of traditional PID control is approximately 85%, while classical Sliding Mode Control (SMC) improves accuracy to 90%-92%. The FLC-ASMC further enhances control accuracy to over 95%, exhibiting the best performance. Experimental results validate that this controller not only effectively controls the pressure difference but also significantly improves the system’s robustness and lifespan, providing a reliable guarantee for the efficient and stable operation of fuel cell systems.
Similar content being viewed by others
Data availability
The data that support the findings of this study are available from the corresponding authorupon reasonable request.
References
Daud, W. R. W. et al. PEM fuel cell system control: A review. Renew. Energy. 113, 620–638 (2017).
Lian, W., et al. Identification of clean energy development routes under carbon emission constraints: A path towards structural adjustment of the power system. Journal of cleaner production, 434. (2024).
Shahzad, K., & Cheema, I. I. Low-carbon technologies in automotive industry and decarbonizing transport. J. Power Sources, 591, 233888 (2024).
Al-Shetwi, A. Q., Hannan, M. A., Jern, K. P., Mansur, M. & Mahlia, T. M. I. Grid-connected renewable energy sources: review of the recent integration requirements and control methods. J. Clean. Prod. 253, 119831 (2020).
Qasem, N. A. & Abdulrahman, G. A. A recent comprehensive review of fuel cells: history, types, and applications. Int. J. Energy Res. 2024 (1), 7271748 (2024).
Sadeghian, O., Oshnoei, A., Mohammadi-Ivatloo, B., Vahidinasab, V. & Anvari-Moghaddam, A. A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges. J. Energy Storage. 54, 105241 (2022).
Al-Durra, A., Yurkovich, S. & Guezennec, Y. Study of nonlinear control schemes for an automotive traction PEM fuel cell system. Int. J. Hydrog. Energy. 35 (20), 11291–11307 (2010).
Zhao, Y. et al. Air and hydrogen supply systems and equipment for PEM fuel cells: A review. Int. J. Green Energy. 19 (4), 331–348 (2022).
Li, C., et al. Gas supply control and experimental validation for polymer electrolyte membrane fuel cells. Mechatronics, 91(1), (2023).
Wei, H., Du, C., Li, X., Shi, C., & Zhang, J. Research on anode pressure control and dynamic performance of proton-exchange membrane fuel cell system for vehicular application. Fuel, 358(PartB), (2024).
Zhou, S. & Jervis, R. A review of polymer electrolyte fuel cells fault diagnosis: progress and perspectives. Chemistry-Methods, 4(1), e202300030 (2024).
Zhao, J., Li, X., Shum, C., & McPhee, J. A review of physics-based and data-driven models for real-time control of polymer electrolyte membrane fuel cells. Energy AI, 6(4),97–129 (2021).
Danzer, M. A., Wilhelm, J., Aschemann, H. & Hofer, E. P. Model-based control of cathode pressure and oxygen excess ratio of a PEM fuel cell system. J. Power Sources. 176 (2), 515–522 (2008).
Song, D., et al. Feedback-linearization decoupling based coordinated control of air supply and thermal management for vehicular fuel cell system. Energy, 305 (2024).
Quan, S., Wang, Y. X., Xiao, X., He, H., & Sun, F. Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells. Applied Energy, 293 (2021).
Jia, Y., Zhang, R., Zhang, T., & Fan, Z. Coordinated control of the fuel cell air supply system based on fuzzy neural. Netw. Decoupling ACS OMEGA, 6(50), 34438–34446 (2021).
Na, W. K., Gou, B. & Diong, B. Nonlinear control of PEM fuel cells by exact linearization. IEEE Trans. Ind. Appl. 43 (6), 1426–1433 (2007).
Liu, Z., Zhang, B. & Xu, S. Research on air mass flow-pressure combined control and dynamic performance of fuel cell system for vehicles application. Appl. Energy. 309, 118446 (2022).
Derbeli, M., Barambones, O., Farhat, M., Ramos-Hernanz, J. A. & Sbita, L. Robust high order sliding mode control for performance improvement of PEM fuel cell power systems. Int. J. Hydrog. Energy. 45 (53), 29222–29234 (2020).
Napole, C., Derbeli, M. & Barambones, O. A global integral terminal sliding mode control based on a novel reaching law for a proton exchange membrane fuel cell system. Appl. Energy. 301, 117473 (2021).
Li, C., Jing, H., Hu, J., Hu, G., & Deng, Y. Sliding mode control for power tracking of Proton-Exchange membrane Fuel-Cell system. J. Energy Eng., 149(3), 04023011 (2023).
Yang, D., Fu, H., Li, J. & Wang, S. A multivariable sliding mode predictive control method for the air management system of automotive fuel cells. Meas. Control. 57 (2), 139–151 (2024).
Peng, C., Xie, C., Zou, J., Jiang, X., & Zhu, Y. A feedback linearization sliding mode decoupling and fuzzy anti-surge compensation based coordinated control approach for PEMFC air supply system. Renewable Energy, 237 (2024).
Yuan, X. et al. Sliding mode controller of hydraulic generator regulating system based on the input/output feedback linearization method. Math. Comput. Simul. 119, 18–34 (2016).
Matas, J., Castilla, M., Guerrero, J. M., de Vicuña, L. G. & Miret, J. Feedback linearization of direct-drive synchronous wind-turbines via a sliding mode approach. IEEE Trans. Power Electron. 23 (3), 1093–1103 (2008).
Meng, J., Guo, Q., Yue, M. & Diallo, D. A Lyapunov-based adaptive control strategy with fault-tolerant objectives for proton exchange membrane fuel cell air supply systems. Appl. Energy. 376, 124275 (2024).
Silaa, M. Y., Bencherif, A. & Barambones, O. A novel robust adaptive sliding mode control using stochastic gradient descent for PEMFC power system. Int. J. Hydrog. Energy. 48 (45), 17277–17292 (2023).
Yin, P., Guo, J. & He, H. Backstep** sliding-mode techniques in current control of polymer electrolyte membrane fuel cell. Control Eng. Pract. 124, 105188 (2022).
Ashok, R. & Shtessel, Y. Control of fuel cell-based electric power system using adaptive sliding mode control and observation techniques. J. Franklin Inst. 352 (11), 4911–4934 (2015).
Souissi, A. Adaptive sliding mode control of a PEM fuel cell system based on the super twisting algorithm[J]. Energy Rep. 7, 3390–3399 (2021).
Jing, H., et al. Multi-Objective sliding mode control of proton exchange membrane fuel cell system based on adaptive algebraic observer. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 238(2), 292–304 (2024).
Rakhtala, S. M. et al. Control of oxygen excess ratio in a PEM fuel cell system using high-order sliding-mode controller and observer[J]. Turkish J. Electr. Eng. Comput. Sci. 23 (1), 255–278 (2015).
Li, Q., Tan, H., Wang, T., Li, X. & Chen, W. An energy management strategy for multistack fuel cell hybrid locomotives with integrated optimization of system service life. IEEE Trans. Transp. Electrification. 11 (2), 5771–5780 (2025).
Li, Q. et al. Health management for PEMFC system Long-Term operation based on optimal temperature trajectory Real-Time optimization. IEEE Trans. Industr. Electron. 72 (3), 2611–2621 (2025).
Pukrushpan, J. T., Peng, H. & Stefanopoulou, A. G. Control-oriented modeling and analysis for automotive fuel cell systems[J]. J. Dyn. Sys Meas. Control. 126 (1), 14–25 (2004).
Gao, W. B. Theory and Design Method of Variable Structure Control (Trans Science, 1996).
Rakhtala, S. M., Ghaderi, R., & Ranjbar Noei, A. Proton exchange membrane fuel cell voltage-tracking using artificial neural networks[J]. Journal of Zhejiang University-Science C (Computers and Electronics), 12(4), 338–344 (2011).
Legala, A., Zhao, J., & Li, X. Machine learning modeling for proton exchange membrane fuel cell performance[J]. Energy and AI, 10(4), 1–16 (2022).
Acknowledgements
This work was supported by Ministry of Education Humanities and Social Sciences ( Project no. 20YJCZH027to FSX), Key topics of the committee of education ministry (Project no. JZW2021030 to FSX), ShanghaiEducational Science Research Program – Philosophy and Social Sciences Research Project for ShanghaiUniversities (Project no. 2024ZSW003 to FSX), Shanghai Key Course Construction: Logistics Economics(Project no. 2023 to FSX, and key course construction of e-commerce in Shanghai Dianji Unive.
Author information
Authors and Affiliations
Contributions
Sixia Fan: Writing – Review & Edit, Writing – Manuscript, Supervision, Visualization, Validation, Methodology, Resources, Project Management, Funding Acquisition. Shuqi Xu: Writing - Review & Editing, Writing - Manuscripts, Software, Visualization, Validation, Methodology, Surveys, Formal Analysis, Data Management, Conceptualization.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Fan, S., Xu, S. Optimal fuel cell control modeling with feedback linearization and adaptive sliding mode control. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35888-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-35888-6
