  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.
