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 article reviews the state of the art of EV energy consumption
models, aiming to provide guidance for the future development of EV
applications. We summarize influential variables of EV energy consumption in
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 utilize machine learning technologies to
estimate EV energy consumption based on a large volume of 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, trucks, and nonroad vehicles), energy estimation models
that are suitable for applications related to vehicle-to-grid integration, and
multiscale energy estimation models as a holistic modeling approach.
