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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.

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.

Impact of driving patterns on fuel economy is significant in hybrid electric vehicles (HEVs). Driving patterns affect propulsion and braking power requirement of vehicles, and they play an essential role in HEV design and control optimization. Driving pattern conscious adaptive strategy can lead to further fuel economy improvement under real-world driving. This paper proposes a real-time driving pattern recognition algorithm for supervisory control under real-world conditions. The proposed algorithm uses reference real-world driving patterns parameterized from a set of representative driving cycles. The reference cycle set consists of five synthetic representative cycles following the real-world driving distance distribution in the US Midwestern region. Then, statistical approaches are used to develop pattern recognition algorithm. Driving patterns are characterized with four parameters evaluated from the driving cycle velocity profiles.