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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>

Hybrid vehicle technology is beginning to make a significant mark in the automotive industry, most notably by the Toyota Prius THS-II and its one-mode technology, but also by two-mode architectures recently introduced. GM-Allison, Renault, and the Timken Company have attempted to capitalize on the advantages over simpler series and parallel architectures that the series-parallel configuration confers on the Prius while also improving the design by allowing the powertrain configuration to physically shift and operate in two different modes depending on the driving load. This work provides an overview of the state-of-the-art in two-mode hybrid vehicle architectures, and demonstrates the performance of this technology in comparison to the market-leading Toyota Prius one-mode hybrid vehicle technology and conventional ICE technology. Simulations in the NREL ADVISOR® software compare the performances of the one- and two-mode architectures against a parallel-full design and the ICE baseline for four different drive cycles and a vehicle with varying weight that simulates a commercial vehicle application. A configuration that is a variation of those designed by GM-Allison was chosen as the representative of the two-mode architectures. The performance metric was fuel economy. The fuel economy was measured over the course of the drive cycles: (1) Urban Dynamometer Driving Schedule for Heavy Duty Vehicles (UDDSHDV); (2) New York City Truck (NYCT); (3) City-Suburban Heavy Vehicle Route (CSHVR); and (4) Highway Fuel Economy Test (HWFET). The vehicle model uses a module developed in-house for a Kenworth T400 truck with a payload that varies from empty to completely full. The results demonstrate that the two-mode architecture provides significantly improved performance to that of the conventional non-hybrid design and comparable performance to that of the parallel-full hybrid design. Furthermore, the one-mode design is shown to be sub-optimal for this vehicle type. Development and optimization of the control strategy, which is the direction of the current research, should allow for additional improvement in fuel economy; optimization of vehicular components could result in improvements in acceleration ability, gradeability, and top speed performance, which lags behind the performance capabilities of the conventional powertrain vehicle in these metrics. The study confirms that two-mode architecture presents unique advantages for constantly changing driving cycles and vehicle payloads and represents the future of hybrid vehicle technology.

Laboratory and on-road vehicle evaluation is conducted on four vehicle models to evaluate and characterize the impacts to fuel economy of real-world auxiliary loads. The four vehicle models in this study include the Volkswagen Jetta TDI, Mazda 3 i-ELOOP, Chevrolet Cruze Diesel, and Honda Civic GX (CNG). Four vehicles of each model are included in this; sixteen vehicles in total. Evaluation was conducted using a chassis dynamometer over standard drive cycles as well as twelve months of on-road driving across a wide range of road and environmental conditions. The information gathered in the study serves as a baseline to quantify future improvements in auxiliary load reduction technology. The results from this study directly support automotive manufacturers in regards to potential "off-cycle" fuel economy credits as part of the Corporate Average Fuel Economy (CAFE) regulations, in which credit is provided for advanced technologies in which reduction of energy consumption from vehicle auxiliary loads can be demonstrated. The observed on-road auxiliary load varied from 135 W to over 1200 W across a wide range of ambient conditions and utilization patterns. The annual average auxiliary load varied across vehicle models from 310 W to 640 W. Ambient temperature was the most predominant factor to impact auxiliary load since air conditioner (A/C) operation is prevalent at high ambient temperature and heating system operation is prevalent at cold ambient temperatures. Additionally the impact of auxiliary load on vehicle fuel economy was determined to be typically between 7.5% and 18% of the fuel consumed during onroad operation of the four vehicle models in this study. During dynamometer testing, auxiliary loads were captured from several key locations along the low-voltage bus, including the alternator output, the low-voltage battery, and select other locations dependent upon the vehicle configuration. Dynamometer testing was then conducted on both certification and custom constant-speed drive cycles at three ambient temperatures (-7 o C, 23 o C, as well as 35 o C with 850 W/m 2 of solar emulation). This instrumentation and test methodology provides an accurate understanding of the energy use by the accessory system from these four vehicle technologies. This paper details and discusses the dynamometer and on-road evaluation results of the auxiliary load from the sixteen vehicles over the twelve month period.

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