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

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

Society of Automotive Engineers (SAE) International Journal of Sustainable Transportation, Energy, Environment, & Policy 2021

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Why This Matters

Accurate energy consumption modeling is essential for the widespread adoption of electric vehicles, as it directly impacts user range anxiety and the viability of optimal fleet scheduling. However, existing models vary significantly in their approaches and applicability to different use cases. This work is innovative because it provides a systematic framework for understanding the landscape of energy consumption models, identifying key limitations in current approaches, and highlighting priorities for future research to support the transition to electric vehicle transportation.

What We Did

This paper presents a comprehensive review of energy consumption estimation models for electric vehicles, examining approaches across different modeling scales (microscopic vs. macroscopic) and methodologies (data-driven vs. rule-based). The review analyzes influential variables in four categories: vehicle components, vehicle dynamics, traffic conditions, and environmental factors. The work classifies existing models and identifies research gaps including the need for models applicable to different vehicle types and approaches suitable for vehicle-to-grid integration applications.

Key Results

The review identifies a trend toward increasingly macroscopic models that can be used at the trip level for energy prediction, combined with growing adoption of data-driven approaches that leverage machine learning. Key findings show that vehicle type, traffic conditions, and weather are critical factors in energy consumption, and that most existing models focus on personal vehicles rather than transit or commercial applications. The review provides guidance for practitioners on model selection based on application requirements.

Full Abstract

Cite This Paper

@article{yuchesae2021,
  author = {Chen, Yuche and Wu, Guoyuan and Sun, Ruixiao and Dubey, Abhishek and Laszka, Aron and Pugliese, Philip},
  journal = {Society of Automotive Engineers (SAE) International Journal of Sustainable Transportation, Energy, Environment, \& Policy},
  title = {A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles},
  year = {2021},
  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 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.},
  contribution = {minor},
  tag = {transit},
  keywords = {electric vehicles, energy consumption, machine learning, transportation modeling, vehicle dynamics, environmental factors}
}
Quick Info
Year 2021
Keywords
electric vehicles energy consumption machine learning transportation modeling vehicle dynamics environmental factors
Research Areas
energy transit ML for CPS
Search Tags

Review, Outlook, Energy, Consumption, Estimation, Models, Electric, Vehicles, electric vehicles, energy consumption, machine learning, transportation modeling, vehicle dynamics, environmental factors, energy, transit, ML for CPS, 2021, Chen, Wu, Sun, Dubey, Laszka, Pugliese