A data driven approach to create an extensible EV driving data model

This paper presents a machine learning approach to analyze driving behaviors that allows a better understanding of electric vehicle. We implement a pattern recognition process to model the driving pattern according to the energy consumption for both a single driver and a fleet. The growing hierarchical self-organizing maps (GHSOM) is applied to learn driver's behaviors gradually, and the experimental results show that the driving behaviors could be recognized with the increase of driving cycles. Moreover, the proposed framework would enhance the understanding of driver's behaviors and also facilitate the EV system design, the big data analytics for IoV and the implementation of advanced driver assistant system (ADAS).