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.
Alali et al. (Wed,) studied this question.