| 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 |
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| ABSTRACT |
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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 |
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Corresponding Author.
Chung-Hong Lee , Email. 
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