Date of Award

2019

Degree Type

Thesis

Degree Name

Master of Science in Systems Engineering

Department

Mechanical, Industrial and Systems Engineering

First Advisor

Gretchen A. Macht

Abstract

Battery electric vehicle (BEV) sales in the United States (US) are constantly growing since 2010, resulting in 238,000 units in 2017. While the impact of various factors on the energy efficiency of internal combustion engines vehicles (ICEVs) was vastly explored in the past, research is recently focusing on BEVs.

The primary, major restraint for electric vehicles is the range limitation. The range of current vehicles on the market vary between 93-315 miles per charge. Combined with the long charging times for batteries, the suitability for daily use is restricted, yet their range is a significant factor of daily requirements of users. Drivers of electric vehicles sometimes fear running out of power, a phenomenon called range anxiety. In order to extend the range of electric vehicles and because BEVs market share is growing globally and nationally, understanding the impact of different range impacting factors (e.g., traffic, temperature, driver behavior) is essential.

The impact of traffic should be understood carefully since it is impressionable by driver behavior, eco-driving strategies, and range management. However, the impact of traffic on the range and, thus, the efficiency of electric vehicles under real-world conditions has not yet entirely transitioned to include BEVs. To quantify the impact of traffic on electric vehicle efficiency, an empirical experiment was conducted with a 2017 eGolf on a predetermined test route. All 30 participants drove the test route twice: once with increased traffic congestion during the morning commute, and once in low traffic congestion during the day. Time was a controlling factor to distinguish two scenarios with different traffic intensities. Finally, both data sets (i.e., traffic data and non-traffic data) were compared to quantify the impact of traffic on the efficiency of BEV under real-world conditions.

Different measures were investigated to provide evidence of differences in the intensity of traffic on the chosen test route based on daytime. Outcomes provide evidence that traffic influences the acceleration and change in acceleration on the test route. A multiple linear regression was applied to quantify the impact of traffic on the difference in state of charge per mile of BEVs. Additionally, driver, temperature and initial state of charge were included in the model and investigated for significance. Results show that among all considered factors, temperature has the highest impact on the energy consumption of BEVs. A stepwise regression was carried and based on the results of both regression models, the influence of traffic could be quantified as to increase the difference in state of charge per mile by up to 2.6% respectively 0.0066 kWh compared to a non-traffic scenario. A logistic regression model was applied to confirm the positive correlation between traffic congestion and BEV energy consumption affirming previous findings.

The investigations based on naturalistic driving data provide new findings about the consumption behavior of BEVs in traffic. These results can help drivers to overcome range anxiety and range limitations by adapting new eco-driving strategies when considering traffic. Additionally, transportation, as well as navigation systems, can be improved. Manufacturers can benefit by using the findings for the development and improvement of electric drive trains and batteries, as well as routing algorithms.

Available for download on Thursday, July 29, 2021

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