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Research on Estimation Method of Fuel Cell Health State Based on Lumped Parameter Model

Author

Listed:
  • Xueshuang Ren

    (Institute of Electrical and Mechanical, Beijing Jiaotong University, Beijing 100044, China)

  • Xin Zhang

    (Institute of Electrical and Mechanical, Beijing Jiaotong University, Beijing 100044, China)

  • Teng Teng

    (Institute of Electrical and Mechanical, Beijing Jiaotong University, Beijing 100044, China)

  • Congxin Li

    (National New Energy Vehicle Technology Innovation Center, Beijing 100044, China)

Abstract

The increasingly serious environmental pollution and the shortage of social energy have promoted the rapid development of fuel cell vehicles. The major factor which limits the commercialization of fuel cell vehicles is durability. Accurately estimating the state and parameters of a fuel cell is critical to extending the life of the fuel cell. To address this challenge, we extended a proton exchange membrane fuel cell (PEMFC) lumped parameter model and incorporated new algorithms that are essential to estimate the health of the fuel cell in a range-extended fuel cell car. The unscented Kalman filter (UKF) algorithm has been used to estimate the ohmic internal resistance of the fuel cell in real time. By using the unscented transformation (UT) method, the linearization of the nonlinear state equation is avoided, and the filtering accuracy is improved without increasing the complexity of the system. By comparing simulation and experimental results, the feasibility and accuracy of the algorithm in this paper are further verified. This method has high estimation accuracy and is suitable for an embedded system. The research of this method is an important basis for improving the control strategy of fuel cell vehicles. Reasonable use of fuel cells can extend battery life, and this method is of great significance to the commercialization of fuel cell vehicles.

Suggested Citation

  • Xueshuang Ren & Xin Zhang & Teng Teng & Congxin Li, 2020. "Research on Estimation Method of Fuel Cell Health State Based on Lumped Parameter Model," Energies, MDPI, vol. 13(23), pages 1-13, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6425-:d:457039
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    References listed on IDEAS

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    1. Zhu, Li & Chen, Junghui, 2018. "Prognostics of PEM fuel cells based on Gaussian process state space models," Energy, Elsevier, vol. 149(C), pages 63-73.
    2. Morando, S. & Jemei, S. & Hissel, D. & Gouriveau, R. & Zerhouni, N., 2017. "ANOVA method applied to proton exchange membrane fuel cell ageing forecasting using an echo state network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 283-294.
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    Cited by:

    1. Ying Tian & Qiang Zou & Jin Han, 2021. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification," Energies, MDPI, vol. 14(7), pages 1-17, March.
    2. Zerong Huang & Daxing Zhang & Xiangdong Wang & Xiaolong Huang & Chunsheng Wang & Liqing Liao & Yaolin Dong & Xiaoshuang Hou & Yuan Cao & Xinyao Zhou, 2024. "Machine Learning Prediction of Fuel Cell Remaining Life Enhanced by Variational Mode Decomposition and Improved Whale Optimization Algorithm," Mathematics, MDPI, vol. 12(19), pages 1-16, September.

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