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Combustion Characterization and Model Fuel Development for Micro-tubular Flame-assisted Fuel Cells
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Internal Characterization-Based Prognostics for Micro-Direct-Methanol Fuel Cells under Dynamic Operating Conditions.

Dacheng Zhang1,2, Xinru Li1, Wei Wang3

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

Sensors (Basel, Switzerland)
|June 10, 2022
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Summary

This study introduces a new method to predict the remaining useful life (RUL) of micro-direct-methanol fuel cells (μDMFCs) by monitoring internal degradation. This approach improves accuracy and precision for these portable power sources.

Keywords:
internal characterizationmicro-direct-methanol fuel celloperating conditionsprognosticsremaining useful life

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Area of Science:

  • Electrochemistry
  • Materials Science
  • Prognostics and Health Management

Background:

  • Micro-direct-methanol fuel cells (μDMFCs) offer high energy density and portability, leveraging micro-electro-mechanical system (MEMS) technology.
  • A key limitation of μDMFCs is their short service life, primarily due to membrane electrode degradation during electrochemical reactions.
  • Accurate prediction of remaining useful life (RUL) is crucial for reliable operation of μDMFCs.

Purpose of the Study:

  • To develop a novel health status assessment and RUL prediction approach for μDMFCs operating under dynamic conditions.
  • To overcome limitations of intrusive external observations by utilizing internal characterization for degradation monitoring.
  • To enhance the accuracy and precision of RUL predictions for μDMFCs.

Main Methods:

  • An internal characterization method was employed to define a degradation indicator, avoiding intrusive external measurements.
  • A Markov-process-based mechanism was developed to predict usage behavior, accounting for operational randomness.
  • The proposed approach was validated through experimental analysis of μDMFC performance under dynamic load.

Main Results:

  • The proposed internal degradation indicator effectively mitigated the impact of user loading profiles on μDMFC output power degradation.
  • The Markov-process-based prediction mechanism accurately captured the stochastic nature of real-world μDMFC operation.
  • Experimental results demonstrated superior prognostic performance in both accuracy and precision compared to traditional external observation methods for RUL prediction.

Conclusions:

  • The developed internal characterization and Markov-process-based prediction method provide a robust approach for μDMFC health monitoring.
  • This non-intrusive prognostics strategy enhances the reliability and extends the effective service life of μDMFCs.
  • The findings offer significant advancements in the field of fuel cell prognostics and health management.