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Control oriented data driven linear parameter varying model for proton exchange membrane fuel cell systems

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  • Deng, Zhihua
  • Chen, Qihong
  • Zhang, Liyan
  • Zong, Yi
  • Zhou, Keliang
  • Fu, Zhichao

Abstract

Proton exchange membrane fuel cell systems are widely used to drive vehicles. It is indispensable to establish accurate and simple control oriented model of the system. However, this control oriented model is difficult to build due to the characteristic of strongly nonlinear, coupled and time - varying in proton exchange membrane fuel cell systems. To solve this problem, nonlinear subspace identification method is proposed to discover linear parameter varying model for the proton exchange membrane fuel cell system. The modeling process can be divided into two stages. In the first stage, algebraic transformation method is proposed to transform the nonlinear model to linear parameter varying model. In the second stage, kernel method based subspace identification is presented to identify parameter matrices of the proton exchange membrane fuel cell system. The established model is demonstrated by a 50 kW on board proton exchange membrane fuel cell system under real driving condition. The results show that mean absolute percent error and root mean squared error are as low as 9.6941e−06 and 0.0957, respectively. Therefore, the control oriented model perfectly agrees with real data. Compared with the prediction error method and recursive neural network nonlinear auto - regressive moving average model with exogenous method, modeling accuracy and speed of the proposed method are the best. Moreover, linear control theory can be employed to design controller based on the linear parameter varying model, which greatly simplifies controller design for proton exchange membrane fuel cell systems.

Suggested Citation

  • Deng, Zhihua & Chen, Qihong & Zhang, Liyan & Zong, Yi & Zhou, Keliang & Fu, Zhichao, 2020. "Control oriented data driven linear parameter varying model for proton exchange membrane fuel cell systems," Applied Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:appene:v:277:y:2020:i:c:s0306261920310527
    DOI: 10.1016/j.apenergy.2020.115540
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    References listed on IDEAS

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    Cited by:

    1. Ke Song & Yimin Wang & Cancan An & Hongjie Xu & Yuhang Ding, 2021. "Design and Validation of Energy Management Strategy for Extended-Range Fuel Cell Electric Vehicle Using Bond Graph Method," Energies, MDPI, vol. 14(2), pages 1-31, January.
    2. José-Luis Casteleiro-Roca & Francisco José Vivas & Francisca Segura & Antonio Javier Barragán & Jose Luis Calvo-Rolle & José Manuel Andújar, 2020. "Hybrid Intelligent Modelling in Renewable Energy Sources-Based Microgrid. A Variable Estimation of the Hydrogen Subsystem Oriented to the Energy Management Strategy," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    3. Deng, Zhihua & Chen, Qihong & Zhang, Liyan & Zhou, Keliang & Zong, Yi & Fu, Zhichao & Liu, Hao, 2021. "Data-driven reconstruction of interpretable model for air supply system of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 299(C).
    4. Fan Yang & Xiaoming Xu & Yuehua Li & Dongfang Chen & Song Hu & Ziwen He & Yi Du, 2023. "A Review on Mass Transfer in Multiscale Porous Media in Proton Exchange Membrane Fuel Cells: Mechanism, Modeling, and Parameter Identification," Energies, MDPI, vol. 16(8), pages 1-24, April.
    5. Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine & Gouriveau, Rafael, 2021. "Proton exchange membrane fuel cell system prognostics and decision-making: Current status and perspectives," Renewable Energy, Elsevier, vol. 179(C), pages 2277-2294.
    6. Lopez-Juarez, M. & Rockstroh, T. & Novella, R. & Vijayagopal, R., 2024. "A methodology to develop multi-physics dynamic fuel cell system models validated with vehicle realistic drive cycle data," Applied Energy, Elsevier, vol. 358(C).
    7. José Agustín Aguilar & Damien Chanal & Didier Chamagne & Nadia Yousfi Steiner & Marie-Cécile Péra & Attila Husar & Juan Andrade-Cetto, 2024. "A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions," Energies, MDPI, vol. 17(2), pages 1-20, January.

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