Work

Evaluation of electric vehicle consumption models based on real-world driving data

Year: 2025

Type: article

Abstract: As the adoption of electric vehicles (EVs) accelerates, understanding and accurately predicting EV energy consumption is essential for optimizing EV range, refining charging infrastructure, and improving overall efficiency. With advances in real-world data collection, this study evaluates seven distinct EV consumption models using driving data from a BMW i3 (60 Ah). The models were assessed based on assumptions, computational methods, and their ability to incorporate factors such as EV dynamics and environmental conditions. Performance metrics—including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²)—were employed to compare model accuracy.The model incorporating detailed resistance forces and constant efficiency factors emerged as the most accurate across all metrics, demonstrating superior performance in predicting energy consumption. Conversely, a model incorporating key resistive forces and efficiency parameters while considering a minimum velocity threshold for power consumption showed the highest error rates. Furthermore, integrating machine learning techniques with physical models proved beneficial, enhancing predictive accuracy with the lowest MAPE and capturing complex patterns in the data, achieving a balance between accuracy and interpretability.This research offers actionable insights into optimizing EV range, designing charging infrastructure, and improving energy efficiency modeling, providing a valuable reference for stakeholders in electric mobility and energy management.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2025.111116

Authors: Daniele Martini, Seyed Mahdi Miraftabzadeh, Nicoletta Matera, Michela Longo, Sonia Leva

Institution: Politecnico di Milano

Language: English

Topic: Electric Vehicles and Infrastructure

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,920