Driving pattern identification for EV range estimation

This paper presents a driving pattern recognition method based on trip segment clustering. Driving patterns categorize various driving behaviors that contain certain energy demand property in common. It can be applied to various applications including intelligent transportation, emission estimation, passive/active safety controls and energy management controls. In this paper, pattern features are first identified from high impact factors from static and quasi-static environmental and traffic information. A feature based trip/route partitioning algorithm is then developed based on data clustering methods. The driving patterns are finally recognized by synthesizing all partitioned feature zones along the trip/route where each partitioned road section is distinguished by an attribute of feature combination that will result in a distinctive drive energy demand property. The driving pattern recognition is a critical technology especially in solving problems like range estimation and energy consumption preplanning for the plug-in capable electrified vehicles.

I. Introduction

In recent years, concerns about environment and energy security put ever-increasingly higher requirements on energy sustainability and efficiency. In the transportation sector, the automotive industry has recognized that vehicle electrification is one of the key technologies toward addressing environmental and energy issues [1]. The development of new grid-enabled passenger vehicles, like electric vehicles (EV), is critical to the future of the industry and global economy. However, technical challenges like EV range and ownership cost have to be overcome before they achieve greater market penetration.