Next Article in Journal
A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles
Next Article in Special Issue
Electrical Power and Energy Systems for Transportation Applications
Previous Article in Journal
Gasification of a Dried Sewage Sludge in a Laboratory Scale Fixed Bed Reactor
Previous Article in Special Issue
Comparative Study of Surface Temperature Behavior of Commercial Li-Ion Pouch Cells of Different Chemistries and Capacities by Infrared Thermography
Article Menu

Export Article

Open AccessArticle
Energies 2015, 8(8), 8573-8593; doi:10.3390/en8088573

Energy Consumption Prediction for Electric Vehicles Based on Real-World Data

Electrotechnical Engineering and Energy Technology, MOBI Research Group, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Paul Stewart
Received: 30 April 2015 / Revised: 30 July 2015 / Accepted: 3 August 2015 / Published: 12 August 2015
(This article belongs to the Special Issue Electrical Power and Energy Systems for Transportation Applications)
View Full-Text   |   Download PDF [898 KB, uploaded 12 August 2015]   |  

Abstract

Electric vehicle (EV) energy consumption is variable and dependent on a number of external factors such as road topology, traffic, driving style, ambient temperature, etc. The goal of this paper is to detect and quantify correlations between the kinematic parameters of the vehicle and its energy consumption. Real-world data of EV energy consumption are used to construct the energy consumption calculation models. Based on the vehicle dynamics equation as underlying physical model, multiple linear regression is used to construct three models. Each model uses a different level of aggregation of the input parameters, allowing predictions using different types of available input parameters. One model uses aggregated values of the kinematic parameters of trips. This model allows prediction with basic, easily available input parameters such as travel distance, travel time, and temperature. The second model extends this by including detailed acceleration data. The third model uses the raw data of the kinematic parameters as input parameters to predict the energy consumption. Using detailed values of kinematic parameters for the prediction in theory increases the link between the statistical model and its underlying physical principles, but requires these parameters to be available as input in order to make predictions. The first two models show similar results. The third model shows a worse fit than the first two, but has a similar accuracy. This model has great potential for future improvement. View Full-Text
Keywords: electric vehicle; energy consumption; real-world data; prediction electric vehicle; energy consumption; real-world data; prediction
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

De Cauwer, C.; Van Mierlo, J.; Coosemans, T. Energy Consumption Prediction for Electric Vehicles Based on Real-World Data. Energies 2015, 8, 8573-8593.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top