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As part of the U.S. Department of Energy's Advanced Vehicle Testing Activity, four new 2012 Nissan Leaf battery electric vehicles were instrumented with data loggers and operated over a fixed onroad test cycle. Each vehicle was operated over the test route, and charged twice daily. Two vehicles were charged exclusively by AC level two electric vehicle supply equipment, while two were exclusively DC fast charged with a 50 kilowatt fast charger. The vehicles were performance tested on a closed test track when new, and after accumulation of 50,000 miles. The traction battery packs were removed and laboratory tested when the vehicles were new, and at 10,000-mile intervals throughout on-road mile accumulation. Battery tests performed include constant-current discharge capacity, electric vehicle pulse power characterization test, and low peak power tests. The data collected over 50,000 miles of driving, charging, and rest are analyzed, including the resulting thermal conditions and power and cycle demands placed upon the battery. Battery performance metrics including capacity, internal resistance, and power capability obtained from laboratory testing throughout the test program are analyzed. Results are compared within and between the two groups of vehicles over the test period. Specifically, the impacts on battery performance, as measured by laboratory and track testing, are explored as they relate to battery usage and variations in conditions encountered, with a primary focus on effects due to the differences between AC level two and DC fast charging. The contrast between battery performance degradation and the effect on vehicle performance is also explored.

<div class="section abstract"><div class="htmlview paragraph">The driving safety performance of automated driving system (ADS)-equipped vehicles (AVs) must be quantified using metrics in order to be able to assess the driving safety performance and compare it to that of human-driven vehicles. In this research, driving safety performance metrics and methods for the measurement and analysis of said metrics are defined and/or developed.</div><div class="htmlview paragraph">A comprehensive literature review of metrics that have been proposed for measuring the driving safety performance of both human-driven vehicles and AVs was conducted. A list of proposed metrics, including novel contributions to the literature, that collectively, quantitatively describe the driving safety performance of an AV was then compiled, including proximal surrogate indicators, driving behaviors, and rules-of-the-road violations. These metrics, which include metrics from on- and off-board data sources, allow the driving safety performance of an AV to be measured in a variety of situations, including crashes, potential conflicts, and near misses. These measurements enable the evaluation of temporal flows and the quantification of key aspects of driving safety performance. The identification and exploration of metrics focusing explicitly on AVs as well as proposing a comprehensive set of metrics is a unique contribution to the literature. The objective is to develop a concise set of metrics that allow driving safety performance assessments to be effectively made and that align with the needs of both the ADS development and transportation engineering communities and accommodate differences in cultural/regional norms.</div><div class="htmlview paragraph">Concurrent project work includes equipping an intersection with a sensor suite of cameras, LIDAR, and RADAR to collect data requiring off-board sources and employing test AVs to collect data requiring on-board sources. Additional concurrent work includes development of artificial intelligence and computer vision-based algorithms to automatically calculate the metrics using the collected data. Future work includes using the collected data and algorithms to finalize the list of metrics and then develop a methodology that uses the metrics to provide an overall driving safety performance assessment score for an AV.</div></div>