A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles

TL;DR

This paper reviews current EV energy consumption models, highlighting trends towards macroscopic and data-driven approaches, and identifies key research gaps for future model development across various vehicle types and applications.

Contribution

It provides a comprehensive classification and analysis of EV energy consumption models, guiding future research directions and addressing existing gaps in the field.

Findings

Increasing use of macroscopic models for trip-level estimation

Growing adoption of machine learning in data-driven models

Identification of research gaps in multi-scale and vehicle-to-grid models

Abstract

Electric vehicles (EVs) are critical to the transition to a low-carbon transportation system. The successful adoption of EVs heavily depends on energy consumption models that can accurately and reliably estimate electricity consumption. This paper reviews the state-of-the-art of EV energy consumption models, aiming to provide guidance for future development of EV applications. We summarize influential variables of EV energy consumption into four categories: vehicle component, vehicle dynamics, traffic and environment related factors. We classify and discuss EV energy consumption models in terms of modeling scale (microscopic vs. macroscopic) and methodology (data-driven vs. rule-based). Our review shows trends of increasing macroscopic models that can be used to estimate trip-level EV energy consumption and increasing data-driven models that utilized machine learning technologies to estimate EV energy consumption based on large volume real-world data. We identify research gaps for EV energy consumption models, including the development of energy estimation models for modes other than personal vehicles (e.g., electric buses, electric trucks, and electric non-road vehicles); the development of energy estimation models that are suitable for applications related to vehicle-to-grid integration; and the development of multi-scale energy estimation models as a holistic modeling approach.