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Research in plug in vehicles (PHEV and BEV) has of course been ongoing for decades, however now that these vehicles are finally being produced for a mass market an intense focus over the last few years has been given to proper evaluation techniques and standard information to effectively convey efficiency information to potential consumers. The first challenge is the development of suitable test procedures. Thanks to many contributions from SAE members, these test procedures have been developed for PHEVs (SAE J1711 now available) and are under development for BEVs (SAE J1634 available later this year). A bigger challenge, however, is taking the outputs of these test results and dealing with the issue of off-board electrical energy consumption in the context of decades-long consumer understanding of MPG as the chief figure of merit for vehicle efficiency.

Software products in the automotive industry are by nature widely distributed and costly to update (recall), so high reliability is clearly of utmost importance. Just as clearly, the increasing reliance on remote access to such systems, for diagnostic and other purposes, has made security an essential requirement, and traditional techniques for software development are proving to be inadequate in dealing with these issues. Correctness by Construction is a software design and development methodology that builds reliability and security into the system from the start. It can be used to demonstrate, with mathematical rigor, a program's correctness properties while reducing the time spent during testing and debugging. This paper will discuss the use of Correctness by Construction, and its accompanying SPARK language technology, to improve automotive systems' security and reliability. (The approach can also account for safely issues, although that is not the focus of this paper.)

Nissan has released our original HEV system in Japan on November 2010, and will release it in US market on March 2011. The 1 motor 2 clutch parallel type using conventional 7 speed automatic transmission has been employed without torque converter and with a manganese cathode and laminated type Li-ion Battery. This system is well recognized its higher efficiency but lower weight and cost, however, has never realized due to technical difficulties of smoothness. At this session, performance achievements and hinged breakthrough technologies will be presented. Presenter Tetsuya Takahashi, Nissan Motor Co., Ltd.

The combination of advanced combustion with advanced selective catalytic reduction (SCR) catalyst formulations was studied in the work presented here to determine the impact of the unique hydrocarbon (HC) emissions from premixed charge compression ignition (PCCI) combustion on SCR performance. Catalyst core samples cut from full size commercial Fe- and Cu-zeolite SCR catalysts were exposed to a slipstream of raw engine exhaust from a 1.9-liter 4-cylinder diesel engine operating in conventional and PCCI combustion modes. The zeolites which form the basis of these catalysts are different with the Cu-based catalyst made on a chabazite zeolite which las smaller pore structures relative to the Fe-based catalyst. Subsequent to exposure, bench flow reactor characterization of performance and hydrocarbon release and oxidation enabled evaluation of overall impacts from the engine exhaust.

The Hybrid Electric Vehicle Team of Virginia Tech participated in the three-year EcoCAR Advanced Vehicle Technology Competition organized by Argonne National Laboratory, and sponsored by General Motors and the U.S. Department of Energy. The team established goals for the design of a plug-in, range-extended hybrid electric vehicle that meets or exceeds the competition requirements for EcoCAR. The challenge involved designing a crossover SUV powertrain to reduce fuel consumption, petroleum energy use, regulated tailpipe emissions, and well-to-wheel greenhouse gas emissions. To interface with and control the hybrid powertrain, the team added a Hybrid Vehicle Supervisory Controller, which enacts a torque split control strategy. This paper builds on an earlier paper [1] that evaluated the petroleum energy use, criteria tailpipe emissions, and greenhouse gas emissions of the Virginia Tech EcoCAR vehicle and control strategy from the 2nd year of the competition.

In support of the U.S Department of Energy's Vehicle Technologies Program, numerous vehicle technology combinations have been simulated using Autonomie. Argonne National Laboratory (Argonne) designed and wrote the Autonomie modeling software to serve as a single tool that could be used to meet the requirements of automotive engineering throughout the development process, from modeling to control, offering the ability to quickly compare the performance and fuel efficiency of numerous powertrain configurations. For this study, a multitude of vehicle technology combinations were simulated for many different vehicles classes and configurations, which included conventional, power split hybrid electric vehicle (HEV), power split plug-in hybrid electric vehicle (PHEV), extended-range EV (E-REV)-capability PHEV, series fuel cell, and battery electric vehicle.

For internal combustion engines and industrial machinery, it is well recognized that the most cost-effective way of reducing energy consumption and extending service life is through lubricant development. This presentation summarizes our recent R&D achievements on developing a new class of candidate lubricants or oil additives ionic liquids (ILs). Features of ILs making them attractive for lubrication include high thermal stability, low vapor pressure, non-flammability, and intrinsic high polarity. When used as neat lubricants, selected ILs demonstrated lower friction under elastohydrodynamic lubrication and less wear at boundary lubrication benchmarked against fully-formulated engine oils in our bench tests. More encouragingly, a group of non-corrosive, oil-miscible ILs has recently been developed and demonstrated multiple additive functionalities including anti-wear and friction modifier when blended into hydrocarbon base oils.