Document Type

Article

Publication Date

2026

DOI

10.3390/smartcities9030050

Publication Title

Smart Cities

Volume

9

Issue

3

Pages

50

Abstract

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously absent in the literature. We identify optimal powertrain efficiency values that are directly derived from publicly available vehicle specifications, ensuring transparency and accessibility. Our findings demonstrate that this simple, physics-based model accurately estimates fuel consumption and CO₂ emissions for standard EPA cycles and can be effectively generalized to user-defined scenarios. This establishes a computationally efficient, interpretable, and robust method for environmental impact assessment, policy evaluation, and real-time emissions estimation.

Rights

© 2026 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors."

Original Publication Citation

Alali, A., & Olariu, S. (2026). Towards supporting real-time estimation of vehicle fuel consumption and CO₂ emissions in smart city applications. Smart Cities, 9(3), Article 50. https://doi.org/10.3390/smartcities9030050

ORCID

0000-0002-3776-216X (Olariu)

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