Date of Award


Degree Type


Degree Name

Master of Science in Systems Engineering


Mechanical, Industrial and Systems Engineering

First Advisor

Gretchen A. Macht


The impact of human behavior on vehicle efficiency has been vastly explored for internal combustion engine (ICE) vehicles. However, human behavioral impacts on vehicle efficiency have not yet transitioned to include battery electric vehicles (BEVs). Understanding the impact of human behavior that achieves BEV efficiency is essential globally, as BEVs begin to retain a significant portion of the automotive market share. BEV sales trends in the US have seen consistent growth since 2010, amounting to over 200,000 units sold by 2015. Globally, the total amount of BEVs and plug-in hybrid electric vehicles (PHEVs) is expected to be 40-70 million by 2025. In light of the growth estimates, defining behavior that induces efficient energy consumption when driving BEVs is essential as these vehicles have a traveling distance constrained to 60-120 miles and can require 1-8 hours to attain a fully charged battery at commercial charging stations.

With firm traveling distances and long charging times, defining human behavioral impacts on BEV efficiency will allow drivers to get the most range out of their vehicle. In order to develop categories of BEV drivers in terms of efficiency, an empirical experiment was conducted to determine if clustering drivers on their energy consumption profiles invokes significant categories. The driving attributes that defined the clusters were extracted to compare whether or not efficient BEV driving is similar to eco-driving in ICE vehicles. Furthermore, BEV drivers can suffer from anxiety that stems from limited traveling distance, a phenomenon known as range anxiety. However, there exist other sources of anxiety-related human driving behavior, three of which can be measured using the driving behavior survey (DBS). The three anxiety measures from the DBS were contrasted against the BEV efficiency clusters found from this research, to determine if the anxiety factors defined by the DBS were responsible for efficient BEV driving.

The results from this research found two significantly different clusters of BEV driving efficiency, which were defined as efficient and inefficient BEV driving. In comparison to eco-driving in ICE vehicles, both aggressive speed and acceleration were found to be contributing factors to BEV efficiency. The results from the DBS proved that anxiety was not a contributing factor to BEV efficiency, as both clusters had similar answers.

The information accumulated through this research can be used to guide new BEV drivers to adopt sustainable driving behaviors, which can help maximize their traveling distance on a single charge. Behavioral contributions to efficiency stemmed mostly from reduction of traveling speed; however, consumption based on elevation and road class selection were also quantified. Drivers can use this information to plan their driving routes to minimize energy usage. Modeling techniques that assume a single rate of energy consumption for the population should include behavioral rates defined by this research. Defining behavioral classes of BEV efficiency is essential as BEV sales are on the rise and drivers and manufactures can both use this information to improve efficiency of these vehicles.



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