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Learning To Recognize Driving Patterns For Collectively Characterizing Electric Vehicle Driving Behaviors
- Source :
- International Journal of Automotive Technology. 20:1263-1276
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
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.
- Subjects :
- business.product_category
Injury control
business.industry
Accident prevention
Computer science
020209 energy
Work (physics)
Poison control
02 engineering and technology
Machine learning
computer.software_genre
Battery management systems
020303 mechanical engineering & transports
0203 mechanical engineering
Power consumption
Automotive Engineering
Electric vehicle
0202 electrical engineering, electronic engineering, information engineering
Artificial intelligence
Unsupervised clustering
business
computer
Subjects
Details
- ISSN :
- 19763832 and 12299138
- Volume :
- 20
- Database :
- OpenAIRE
- Journal :
- International Journal of Automotive Technology
- Accession number :
- edsair.doi...........d8a1515eb560bf2c06e0e3247e40d31a