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SAE 2011 High Efficiency IC Engines Symposium - Session 1- Pathways to High Efficiency. Presenter John E. Dec, Sandia National Laboratories

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.

High-load HCCI combustion has recently been demonstrated with conventional gasoline using intake pressure boosting. The key is to control the high combustion heat release rates (HRR) by using combustion timing retard and mixture stratification. However, at naturally aspirated and moderately boosted conditions, these techniques did not work well due to the low autoignition reactivity of conventional gasoline at these conditions. This work studies a low-octane distillate fuel with similar volatility to gasoline, termed Hydrobate, for its potential in HCCI engine combustion at naturally aspirated and low-range boosted conditions. The HCCI combustion with fully premixed and partially stratified charges was examined at intake pressures (Pin) from 100 to 180 kPa and constant intake temperature (60�C) and engine speed (1200 rpm).