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Currently, two consolidated aftertreatment technologies are available for the reduction of NOx emissions from diesel engines: Urea SCR (Selective Catalytic Reduction) systems and LNT (Lean NOx Trap) systems. Urea SCR technology, which has been widely used for many years at stationary sources, is becoming nowadays an attractive alternative also for light-duty diesel applications. However, SCR systems are much more effective in NOx reduction efficiency at high load operating conditions than light load condition, characterized by lower exhaust gas temperatures.

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.

A new index for evaluating load path dispersion is proposed, using a structural load path analysis method based on the concept of U* , which expresses the connection strength between a load point and an arbitrary point within the structure enables the evaluation of the load path dispersion within the structure by statistical means such as histograms and standard deviations. Presenter Tadashi Naito, Honda R&D Co., Ltd.

Honda's technology applied to CR-Z HEV propulsion system. Presenter Takeo Nishibori, Honda R&D Co., Ltd.