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

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

Dubey, Abhishek / Laszka, Aron / Wu, Guoyuan / Pugliese, Philip / Sun, Ruixiao / Chen, Yuche

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.