Predictive Battery Load Forecasting in Electric Fleets Hybridized with Methanol-Derived Hydrogen Fuel Cells

Stephanie Yen Nee Kew

doi:10.30939/ijastech..1814700

Predictive Battery Load Forecasting in Electric Fleets Hybridized with Methanol-Derived Hydrogen Fuel Cells

Abstract

Rising demand for sustainable transport is driving the adoption of hybrid electric vehicles (EVs), with batteries complemented by MDHFCs to support high-load and extended-range performance. This study proposes a conceptual predictive control framework for battery load forecasting in MDHFC-hybridized EV fleets. The framework integrates model predictive control (MPC), fuzzy logic, and long short-term memory (LSTM) forecasting to coordinate energy sources in real time. A dynamic forecasting architecture processes time-series inputs, including auxiliary load, vehicle speed, state of charge (SoC), route gradient, and ambient environmental variables, improving responsiveness and ensuring reliable performance under real-world conditions. Energy contributions are adjusted via matrix-based logic using a dynamic α factor, and variable-rate telemetry enhances accuracy during transient load fluctuations. Simulation-based sensitivity analyzes and scenario testing evaluate system robustness across diverse driving patterns, energy demands, and hydrogen consumption rates. Future adaptations may incorporate drive-cycle feedback and reinforcement learning (RL) to refine matrix logic. Compared to static-rule methods, this approach is conceptually predicted to enhance hydrogen utilization by ~8% and reduce battery current fluctuation by ~13%, promoting intelligent, energy-efficient, and scalable energy coordination in hybrid EV fleets. The findings are intended to inform practical deployment strategies and guide future optimization of hybrid EV energy management architectures.

Keywords

References

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Details

Primary Language

English

Subjects

Hybrid and Electric Vehicles and Powertrains

Journal Section

Research Article

Authors

Stephanie Yen Nee Kew ^*
0009-0001-2957-8848
Malaysia

Publication Date

February 11, 2026

Submission Date

October 31, 2025

Acceptance Date

January 9, 2026

Published in Issue

Year 2026 Volume: 10 Number: 1

DOI

https://doi.org/10.30939/ijastech..1814700

IZ

https://izlik.org/JA26AT84PC

Cite

RIS / Bibtex

APA

Kew, S. Y. N. (2026). Predictive Battery Load Forecasting in Electric Fleets Hybridized with Methanol-Derived Hydrogen Fuel Cells. International Journal of Automotive Science And Technology, 10(1), 1-25. https://doi.org/10.30939/ijastech..1814700

AMA

1.Kew SYN. Predictive Battery Load Forecasting in Electric Fleets Hybridized with Methanol-Derived Hydrogen Fuel Cells. IJASTECH. 2026;10(1):1-25. doi:10.30939/ijastech.1814700

Chicago

Kew, Stephanie Yen Nee. 2026. “Predictive Battery Load Forecasting in Electric Fleets Hybridized With Methanol-Derived Hydrogen Fuel Cells”. International Journal of Automotive Science And Technology 10 (1): 1-25. https://doi.org/10.30939/ijastech. 1814700.

EndNote

Kew SYN (February 1, 2026) Predictive Battery Load Forecasting in Electric Fleets Hybridized with Methanol-Derived Hydrogen Fuel Cells. International Journal of Automotive Science And Technology 10 1 1–25.

IEEE

[1]S. Y. N. Kew, “Predictive Battery Load Forecasting in Electric Fleets Hybridized with Methanol-Derived Hydrogen Fuel Cells”, IJASTECH, vol. 10, no. 1, pp. 1–25, Feb. 2026, doi: 10.30939/ijastech..1814700.

ISNAD

Kew, Stephanie Yen Nee. “Predictive Battery Load Forecasting in Electric Fleets Hybridized With Methanol-Derived Hydrogen Fuel Cells”. International Journal of Automotive Science And Technology 10/1 (February 1, 2026): 1-25. https://doi.org/10.30939/ijastech. 1814700.

JAMA

1.Kew SYN. Predictive Battery Load Forecasting in Electric Fleets Hybridized with Methanol-Derived Hydrogen Fuel Cells. IJASTECH. 2026;10:1–25.

MLA

Kew, Stephanie Yen Nee. “Predictive Battery Load Forecasting in Electric Fleets Hybridized With Methanol-Derived Hydrogen Fuel Cells”. International Journal of Automotive Science And Technology, vol. 10, no. 1, Feb. 2026, pp. 1-25, doi:10.30939/ijastech. 1814700.

Vancouver

1.Stephanie Yen Nee Kew. Predictive Battery Load Forecasting in Electric Fleets Hybridized with Methanol-Derived Hydrogen Fuel Cells. IJASTECH. 2026 Feb. 1;10(1):1-25. doi:10.30939/ijastech. 1814700