Big-data framework for electric vehicle range estimation

Range anxiety is a major contributor in low penetration of electric vehicles into the transportation market. Although several methods have been developed to estimate the remaining charge of the battery, the remaining driving range is a parameter that is related to different standard, historical, and real-time data. Most of the existing range estimation approaches are established on an overly simplified model that relies on a limited collection of data. However, the sensitivity and reliability of the range estimation algorithm changes under different environmental and operating conditions; and it is necessary to have a structure that is able to consider all data related to the range estimation. In this paper, we propose a big-data based range estimation framework that is able to collect different data with various structures from numerous resources; organize and analyze the data, and incorporate them in the range estimation algorithm. MATLAB/SIMULINK code is demonstrated to read real-time and historical data from different web databases and calculate the remaining driving range.

I. Introduction

Electric Vehicles (EVs) are now financially within reach of many American families, yet their adoption rate remains low in the US (3.38 % of new cars were EVs in 2012). Industry research has uncovered that “the anxiety felt by many drivers about the remaining driving range their vehicle can run before the next charge” is a major contributing factor to this low adoption rate [1]. This anxiety is mainly because the current technologies cannot accurately estimate the remaining driving range of EVs.