Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Retraction Note: Plant disease recognition using residual convolutional enlightened Swin transformer networks.

Scientific reports·2026

Same author

An improved crayfish optimization algorithm for solving engineering optimization problems.

PloS one·2026

Same author

Multi-strategy remora optimization algorithm for color multi-threshold image segmentation.

PloS one·2026

Same author

Corrigendum to "Greater cane rat algorithm (GCRA): A nature-inspired metaheuristic for optimization problems" [Heliyon Volume 10, Issue 11, June 2024, Article e31629].

Heliyon·2025

Same author

Improved COOT optimization: An approach to multilevel thresholding in image segmentation.

Scientific reports·2025

Same author

Innovative animal models for surgical interventions and implant biocompatibility: a translational perspective.

Annals of medicine and surgery (2012)·2025

Search research articles

Related Experiment Video

Updated: Jun 6, 2025

On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method

On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method

Published on: March 16, 2018

Precision parameter estimation in Proton Exchange Membrane Fuel Cells using depth information enhanced Differential

Pradeep Jangir^1,2,3,4,5,6, Absalom E Ezugwu⁷, Arpita⁸

¹University Centre for Research and Development, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India.

Scientific Reports

|November 28, 2024

Summary

This summary is machine-generated.

A new Depth Information-Based Differential Evolution (Di-DE) algorithm accurately estimates parameters for Proton Exchange Membrane Fuel Cell (PEMFC) models. This advanced method improves PEMFC design and performance, aiding clean energy applications.

Keywords:

Parameter estimation Differential Evolution Optimization PEMFC Proton Exchange Membrane Fuel Cell

More Related Videos

A Guide to Concentration Alternating Frequency Response Analysis of Fuel Cells

A Guide to Concentration Alternating Frequency Response Analysis of Fuel Cells

Published on: December 11, 2019

Probing and Mapping Electrode Surfaces in Solid Oxide Fuel Cells

Probing and Mapping Electrode Surfaces in Solid Oxide Fuel Cells

Published on: September 20, 2012

Related Experiment Videos

Last Updated: Jun 6, 2025

On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method

On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method

Published on: March 16, 2018

A Guide to Concentration Alternating Frequency Response Analysis of Fuel Cells

A Guide to Concentration Alternating Frequency Response Analysis of Fuel Cells

Published on: December 11, 2019

Probing and Mapping Electrode Surfaces in Solid Oxide Fuel Cells

Probing and Mapping Electrode Surfaces in Solid Oxide Fuel Cells

Published on: September 20, 2012

Area of Science:

* Energy Science and Engineering
* Computational Modeling and Optimization

Background:

* Accurate modeling of Proton Exchange Membrane Fuel Cells (PEMFCs) is crucial for design and performance enhancement.
* Parameter estimation in PEMFC models presents a complex, nonlinear optimization challenge.

Purpose of the Study:

* To apply and evaluate the novel Depth Information-Based Differential Evolution (Di-DE) algorithm for PEMFC parameter estimation.
* To compare the performance of Di-DE against established evolutionary algorithms in optimizing PEMFC models.

Main Methods:

* Implementation of the Depth Information-Based Differential Evolution (Di-DE) algorithm.
* Testing Di-DE on twelve diverse PEMFC models, including BCS 500 W, Nedstack 600 W, and various 250W and 12W configurations.
* Comparative analysis against algorithms such as iwPSO, CLPSO, DNLPSO, SLPSO, SaDE, SHADE, JADE, QUATRE, LSA, QUATRE-EMS, and C-QUATRE.

Main Results:

* Di-DE achieved a minimum objective function value of 0.0255 with an average runtime improvement of 98.8% compared to other algorithms.
* Optimized parameters resulted in Sum of Squared Errors (SSE) as low as 0.00002, demonstrating high precision and stability.
* Predicted voltage-current (V-I) and power-voltage (P-V) characteristics showed less than 1% error against experimental data for all tested PEMFCs.

Conclusions:

* The Di-DE algorithm is a highly accurate and efficient metaheuristic approach for PEMFC parameter estimation.
* Di-DE's performance surpasses existing evolutionary algorithms, offering significant improvements in runtime and accuracy.
* This work demonstrates Di-DE's potential for advanced PEMFC modeling, supporting the wider adoption of fuel cell technology in clean energy solutions.