Research on Estimation Method of Mileage Power Consumption for Electric Vehicles

DOI

10.2991/csece-18.2018.110How to use a DOI?

Keywords

electric vehicles; mileage power consumption estimation; driving cycle identification; driving cycle prediction

Abstract

Accurate estimation of the power consumption of electric vehicles in the future driving route will help to reduce the user's range anxiety, which results from short all electric range of electric vehicles and the lack of charging facilities. The existing research methods mainly focus on three aspects: vehicle energy consumption, driving cycle identification and driving cycle prediction. In this paper, a method of estimating mileage power consumption based on driving cycle identification and prediction is proposed. Firstly, driving cycle categories and energy consumption of each category are obtained through conducting screening, sectioning, principal component analysis and fuzzy C clustering to the vehicle's historical operation data. Then, the future vehicle speed curve is predicted based on historical data, elevation information and real-time road congestion information, and the mileage consumption estimation model is established based on the identification and prediction of the driving cycle. Finally, 10 groups of real vehicle tests were carried out on the experimental vehicle. And the results showed that the average error between the estimated value of the mileage power consumption and the test value is 4.15%, which met the requirements of the daily use of electric vehicles.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yang Xu
AU  - Kaiyu Wang
PY  - 2018/02
DA  - 2018/02
TI  - Research on Estimation Method of Mileage Power Consumption for Electric Vehicles
BT  - Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
PB  - Atlantis Press
SP  - 504
EP  - 508
SN  - 2352-538X
UR  - https://doi.org/10.2991/csece-18.2018.110
DO  - 10.2991/csece-18.2018.110
ID  - Xu2018/02
ER  -