| Home | KSAE | E-Submission | Sitemap | Contact Us |  
top_img
International Journal of Automotive Technology > Volume 20(6); 2019 > Article
International Journal of Automotive Technology 2019;20(6): 1263-1276.
doi: https://doi.org/10.1007/s12239-019-0118-4
LEARNING TO RECOGNIZE DRIVING PATTERNS FOR COLLECTIVELY CHARACTERIZING ELECTRIC VEHICLE DRIVING BEHAVIORS
Chung-Hong Lee, Chih-Hung Wu
National Kaohsiung University of Science and Technology
PDF Links Corresponding Author.  Chung-Hong Lee , Email. leechung@mail.ee.nkust.edu.tw
ABSTRACT
As electric vehicle (EV) emerges, it is important to understand how driver’s driving behavior is influencing power consumption in an electric vehicle. Driver’s personal driving behavior is usually quite distinctive and can be recognized by means of driving patterns after some driving cycles. This paper presents a method combining several machine learning approaches to characterize driving behaviors of electric vehicles. The driving patterns are modeled according to power consumption monitored by the battery management system (BMS), in aspects of individual driver’s personal and EV-fleet operations. First, we apply an unsupervised clustering approach to characterize a driver's behaviors by formulating driving patterns. Subsequently, the resulting clustered datasets were used to train machine-learning based classifiers for classification of dataset of EV and EV-fleet driving patterns. The work aims to provide a robust solution to help identify the characteristics of specific types of EVs and their driver behaviors, in order to allow automakers and EV-subsystem providers to gather valuable driving information for product improvement.
Key Words: Electric vehicles, Data mining, Energy management, Battery management systems, Machine learning
TOOLS
Preview  Preview
Full text via DOI  Full text via DOI
Download Citation  Download Citation
CrossRef TDM  CrossRef TDM
  E-Mail
  Print
Share:      
METRICS
0
Scopus
405
View
18
Download
Related article
Editorial Office
21 Teheran-ro 52-gil, Gangnam-gu, Seoul 06212, Korea
TEL: +82-2-564-3971   FAX: +82-2-564-3973   E-mail: car@ksae.org
About |  Browse Articles |  Current Issue |  For Authors and Reviewers
Copyright © The Korean Society of Automotive Engineers. All rights reserved.                
Close layer
prev next