Skip to main content

Hydrogen Fuel Cells Lifetime Prediction Based on Multi-layer Perceptron

  • Conference paper
  • First Online:
Energy Power and Automation Engineering (ICEPAE 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1118))

Included in the following conference series:

  • 157 Accesses

Abstract

Hydrogen fuel cells lifetime is essential for vehicles utilization and machines, which has been concerned by existing researchers. However, existing prediction is based on mathematical calculation and leaks the parameters of other situations including using situation and cells surroundings. This paper presents a novel approach to predict the lifetime of hydrogen fuel cells using a multi-layer perceptron (MLP) model by measuring the cell voltages. The lifetime of hydrogen fuel cells is a critical factor in their successful application and deployment. In this work, a MLP model is utilized to predict the lifetime of hydrogen fuel cells based on various input parameters including temperature, voltage and current information. The model is trained and tested on a dataset of experimental results from a laboratory-scale hydrogen fuel cell. The results demonstrate that the proposed MLP model is able to accurately predict the lifetime of hydrogen fuel cells with a mean absolute error of 0.17 years. This approach is promising for the development of hydrogen fuel cell technology and could be used to optimize the design and operation of fuel cells.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Lin R-H, Xi X-N, Wang P-N, Wu B-D, Tian S-M (2018) Review on hydrogen fuel cell condition monitoring and prediction methods. Int J Hydrogen Energy

    Google Scholar 

  2. Quan S, Wang Y-X, Xiao X, He H, Sun F (2021) Disturbance prediction-based enhanced stochastic model predictive control for hydrogen supply and circulating of vehicular fuel cells. Energy Convers Manag

    Google Scholar 

  3. Fu Z, Chen Q, Zhang L, Fan J, Zhang H, Deng Z (2021) Research on energy management strategy of fuel cell power generation system based on Grey–Markov chain power prediction. Energy Rep 7(Supplement 1)

    Google Scholar 

  4. Long B, Wu K, Li P, Li M (2022) A novel remaining useful life prediction method for hydrogen fuel cells based on the gated recurrent unit neural network. Appl Sci

    Google Scholar 

  5. Huang D-W, Qi D-Q, Yu N (2016) Capacity allocation of hydrogen production and fuel cells in wind farm based on stochastic prediction error. In: International conference on energy development and environmental protection (EDEP 2016)

    Google Scholar 

  6. Shi Y, Xiao J, Quan S, Pan M, Zhang L (2009) Fractal model for prediction of effective hydrogen diffusivity of gas diffusion layer in proton exchange membrane fuel cell. Int J Hydrogen Energy

    Google Scholar 

  7. Zhang B, Pang L, Shen X, Gao Y (2016) Measurement and prediction of detonation cell size in binary fuel blends of methane/hydrogen mixtures. Fuel

    Google Scholar 

  8. Hydrogen; researchers from Russian Academy of Science detail new studies and findings in the area of hydrogen (lifetime prediction for the hydrogen-air fuel cells). Chemicals & Chemistry (2015)

    Google Scholar 

  9. Pei P, Chen D, Wu Z, Ren P (2019) Nonlinear methods for evaluating and online predicting the lifetime of fuel cells. Appl Energy

    Google Scholar 

  10. Lin X, Wang Z, Wu J (2020) Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle. Int J Energy Res

    Google Scholar 

  11. Hu X, Zou C, Tang X, Liu T, Hu L (2020) Cost-optimal energy management of hybrid electric vehicles using fuel cell/battery health-aware predictive control. IEEE Trans Power Electron

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaokai Zhou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhou, X., Liu, Q. (2024). Hydrogen Fuel Cells Lifetime Prediction Based on Multi-layer Perceptron. In: Yadav, S., Arya, Y., Muhamad, N.A., Sebaa, K. (eds) Energy Power and Automation Engineering. ICEPAE 2023. Lecture Notes in Electrical Engineering, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-99-8878-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8878-5_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8877-8

  • Online ISBN: 978-981-99-8878-5

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics