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

arXiv: https://arxiv.org/abs/2003.12873 · HTML: https://mlsanity.com/paper/2003.12873/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [A Review on AI Algorithms for Energy Management in E-Mobility Services](https://mlsanity.com/paper/2309.15140/) (2023)
- [Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning](https://mlsanity.com/paper/2312.07371/) (2023)
- [Artificial Intelligence for Smart Transportation](https://mlsanity.com/paper/2308.07457/) (2023)
- [Optimal Design and Implementation of an Open-source Emulation Platform for User-Centric Shared E-mobility Services](https://mlsanity.com/paper/2403.07964/) (2024)
- [Energy Estimation of Last Mile Electric Vehicle Routes](https://mlsanity.com/paper/2408.12006/) (2024)
- [A Hybrid Surrogate for Electric Vehicle Parameter Estimation and Power Consumption via Physics-Informed Neural Operators](https://mlsanity.com/paper/2508.12602/) (2025)

## Abstract (arXiv)

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.