A Machine Learning Approach for Evaluating Operating Parameters in Proton Exchange Membrane Fuel Cells Using Automotive Dynamic Test Data: Conceptual Consideration for Unmanned Aerial Vehicle Applications

Hasan Çınar; Süleyman Tunçel

doi:10.30518/jav.2025203

A Machine Learning Approach for Evaluating Operating Parameters in Proton Exchange Membrane Fuel Cells Using Automotive Dynamic Test Data: Conceptual Consideration for Unmanned Aerial Vehicle Applications

Abstract

Owing to their elevated energy density and rapid refuelling capability, proton exchange membrane fuel cells (PEMFCs) hold strong potential as a primary power source for fully electric ground and aerial vehicles, while also generating zero greenhouse gas emissions. Nevertheless, accurately estimating PEMFC state of health (SOH) continues to pose a major obstacle to their broader adoption in electric vehicle applications. Given that PEMFC health is directly linked to essential operating parameters, particularly voltage and power output, continuous monitoring is essential for ensuring safe, dependable vehicle operation. Motivated by this need, the present work implements sophisticated data-driven machine learning techniques to estimate PEMFC output power as a basis for health monitoring. In particular, CatBoost, Decision Tree, and Random Forest algorithms were applied to a publicly accessible dataset obtained under an automotive dynamic driving cycle. Findings indicate that all three algorithms deliver low prediction error when estimating output power. Beyond this, the study provides a thorough comparison of these three machine learning approaches and discusses at a conceptual level how the proposed technique could be incorporated into energy management and trajectory planning systems for PEMFC-driven unmanned aerial vehicles.

Keywords

Supporting Institution

Not applicable.

Project Number

Not applicable.

Ethical Statement

Not applicable. The manuscript does not contain clinical studies or patient data.

Thanks

Not applicable.

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Details

Primary Language

English

Subjects

Deep Learning, Aerospace Engineering (Other)

Journal Section

Research Article

Authors

Hasan Çınar ^*
0000-0001-8718-3767
Türkiye

Süleyman Tunçel This is me
0000-0003-4565-2590
Türkiye

Early Pub Date

October 19, 2026

Publication Date

October 25, 2026

Submission Date

August 25, 2026

Acceptance Date

October 13, 2026

Published in Issue

Year 2026 Volume: 10 Number: 3

DOI

https://doi.org/10.30518/jav.2025203

IZ

https://izlik.org/JA37PS58RR

Cite

RIS / Bibtex

APA

Çınar, H., & Tunçel, S. (2026). A Machine Learning Approach for Evaluating Operating Parameters in Proton Exchange Membrane Fuel Cells Using Automotive Dynamic Test Data: Conceptual Consideration for Unmanned Aerial Vehicle Applications. Journal of Aviation, 10(3), 470-479. https://doi.org/10.30518/jav.2025203

AMA

1.Çınar H, Tunçel S. A Machine Learning Approach for Evaluating Operating Parameters in Proton Exchange Membrane Fuel Cells Using Automotive Dynamic Test Data: Conceptual Consideration for Unmanned Aerial Vehicle Applications. JAV. 2026;10(3):470-479. doi:10.30518/jav.2025203

Chicago

Çınar, Hasan, and Süleyman Tunçel. 2026. “A Machine Learning Approach for Evaluating Operating Parameters in Proton Exchange Membrane Fuel Cells Using Automotive Dynamic Test Data: Conceptual Consideration for Unmanned Aerial Vehicle Applications”. Journal of Aviation 10 (3): 470-79. https://doi.org/10.30518/jav.2025203.

EndNote

Çınar H, Tunçel S (October 1, 2026) A Machine Learning Approach for Evaluating Operating Parameters in Proton Exchange Membrane Fuel Cells Using Automotive Dynamic Test Data: Conceptual Consideration for Unmanned Aerial Vehicle Applications. Journal of Aviation 10 3 470–479.

IEEE

[1]H. Çınar and S. Tunçel, “A Machine Learning Approach for Evaluating Operating Parameters in Proton Exchange Membrane Fuel Cells Using Automotive Dynamic Test Data: Conceptual Consideration for Unmanned Aerial Vehicle Applications”, JAV, vol. 10, no. 3, pp. 470–479, Oct. 2026, doi: 10.30518/jav.2025203.

ISNAD

Çınar, Hasan - Tunçel, Süleyman. “A Machine Learning Approach for Evaluating Operating Parameters in Proton Exchange Membrane Fuel Cells Using Automotive Dynamic Test Data: Conceptual Consideration for Unmanned Aerial Vehicle Applications”. Journal of Aviation 10/3 (October 1, 2026): 470-479. https://doi.org/10.30518/jav.2025203.

JAMA

1.Çınar H, Tunçel S. A Machine Learning Approach for Evaluating Operating Parameters in Proton Exchange Membrane Fuel Cells Using Automotive Dynamic Test Data: Conceptual Consideration for Unmanned Aerial Vehicle Applications. JAV. 2026;10:470–479.

MLA

Çınar, Hasan, and Süleyman Tunçel. “A Machine Learning Approach for Evaluating Operating Parameters in Proton Exchange Membrane Fuel Cells Using Automotive Dynamic Test Data: Conceptual Consideration for Unmanned Aerial Vehicle Applications”. Journal of Aviation, vol. 10, no. 3, Oct. 2026, pp. 470-9, doi:10.30518/jav.2025203.

Vancouver

1.Hasan Çınar, Süleyman Tunçel. A Machine Learning Approach for Evaluating Operating Parameters in Proton Exchange Membrane Fuel Cells Using Automotive Dynamic Test Data: Conceptual Consideration for Unmanned Aerial Vehicle Applications. JAV. 2026 Oct. 1;10(3):470-9. doi:10.30518/jav.2025203