1. Learning To Recognize Driving Patterns For Collectively Characterizing Electric Vehicle Driving Behaviors
- Author
-
Chih-Hung Wu and Chung-Hong Lee
- 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 - 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.
- Published
- 2019