Statistical Characterization of Medium-Duty Electric Vehicle Drive Cycles: Preprint

In an effort to help commercialize technologies for electric vehicles (EVs) through deployment and demonstration projects, the U.S. Department of Energy's (DOE's) American Recovery and Reinvestment Act (ARRA) provided funding to participating U.S. companies to cover part of the cost of purchasing new EVs. Within the medium- and heavy-duty commercial vehicle segment, both Smith Electric Newton and and Navistar eStar vehicles qualified for such funding opportunities. In an effort to evaluate the performance characteristics of the new technologies deployed in these vehicles operating under real world conditions, data from Smith Electric and Navistar medium-duty EVs were collected, compiled, and analyzed by the National Renewable Energy Laboratory's (NREL) Fleet Test and Evaluation team over a period of 3 years. More than 430 Smith Newton EVs have provided data representing more than 150,000 days of operation. Similarly, data have been collected from more than 100 Navistar eStar EVs, resulting in a comparative total of more than 16,000 operating days. Combined, NREL has analyzed more than 6 million kilometers of driving and 4 million hours of charging data collected from commercially operating medium-duty electric vehicles in various configurations. In this paper, extensive duty-cycle statistical analyses are performed to examine and characterize common vehicle dynamics trends and relationships based on in-use field data. The results of these analyses statistically define the vehicle dynamic and kinematic requirements for each vehicle, aiding in the selection of representative chassis dynamometer test cycles and the development of custom drive cycles that emulate daily operation. In this paper, the methodology and accompanying results of the duty-cycle statistical analysis are presented and discussed. Results are presented in both graphical and tabular formats illustrating a number of key relationships between parameters observed within the data set that relate to medium duty EVs.