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Batteries and Fuel Cells03:12

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A battery is a galvanic cell that is used as a source of electrical power for specific applications. Modern batteries exist in a multitude of forms to accommodate various applications, from tiny button batteries such as those that power wristwatches to the very large batteries used to supply backup energy to municipal power grids. Some batteries are designed for single-use applications and cannot be recharged (primary cells), while others are based on conveniently reversible cell reactions that...
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A parameter estimation method for modelling proton exchange membrane fuel cell based on enhanced meta evolutionary

Vivekananda Pattanaik1,2, Shubhranshu Mohan Parida3, Binaya Kumar Malika1,4

  • 1Department of Electrical Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751030, India.

Scientific Reports
|March 10, 2026
PubMed
Summary
This summary is machine-generated.

Accurate parameter estimation for proton exchange membrane fuel cells (PEMFCs) is vital for reliable modeling. An enhanced meta-evolutionary differential evolution algorithm (EMEDEA) offers superior accuracy and effectiveness in PEMFC parameter estimation.

Keywords:
Differential evolutionMeta evolutionary algorithmObjective functionParameter estimationProton exchange membrane fuel cells

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Area of Science:

  • * Energy Systems Engineering
  • * Computational Science
  • * Electrochemical Engineering

Background:

  • * Accurate modeling of proton exchange membrane fuel cells (PEMFCs) is essential for simulations and control.
  • * Conventional parameter estimation methods struggle with the inherent non-linearity and complexity of PEMFCs.
  • * Hybrid algorithms show promise but can be further optimized for improved performance.

Purpose of the Study:

  • * To propose an enhanced meta-evolutionary differential evolution algorithm (EMEDEA) for PEMFC parameter estimation.
  • * To improve the accuracy and reliability of PEMFC models through advanced optimization techniques.
  • * To address the challenges of non-linearity and multi-variable complexity in PEMFC systems.

Main Methods:

  • * Development of the enhanced meta-evolutionary differential evolution algorithm (EMEDEA) with dynamic parameter tuning.
  • * Utilization of a non-linear mathematical model of PEMFCs for real-time feasibility.
  • * Formulation of the objective function based on the sum of squared deviations between experimental and estimated stack voltages.

Main Results:

  • * EMEDEA demonstrated high accuracy and effectiveness in estimating PEMFC parameters across three different stacks.
  • * Comparative analysis confirmed the superiority of EMEDEA over other prominent estimation methods.
  • * Convergence graphs indicated swift attainment of optimal parameter values.

Conclusions:

  • * The proposed EMEDEA is a reliable and accurate method for PEMFC parameter estimation.
  • * Dynamic parameter tuning enhances the performance of differential evolution strategies.
  • * This approach significantly advances the precision of PEMFC modeling for practical applications.