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Engine downsizing can effectively improve the fuel economy of spark ignition (SI) gasoline engines, but extreme downsizing is limited by knocking combustion and low-speed pre-ignition at higher loads. A 2-stroke SI engine can produce higher upper load compared to its naturally aspirated 4-stroke counterpart with the same displacement due to the double firing frequency at the same engine speed. To determine the potential of a downsized two-cylinder 2-stroke poppet valve SI gasoline engine with 0.7 L displacement in place of a naturally aspirated 1.6 L gasoline (NA4SG) engine, one-dimensional models for the 2-stroke gasoline engine with a single turbocharger and a two-stage supercharger-turbocharger boosting system were set up and validated by experimental results.

The fuel economy of plug-in hybrid electric city bus (PHEV) is deeply affected by driving cycle and travel distance. To improve the adaption of energy management strategy, the equivalent coefficient of fuel is the key parameter that needs to be pre-optimized based on the predicted driving cycle. An iterative learning method was proposed and implemented in order to get the best equivalent coefficient based on the predicted driving cycle and battery capacity. In the iterative learning method, the energy model and kinematics model of the bus were built. The ECMS (Equivalent Consumption Minimization Strategy) method was applied to obtain the best fuel economy with the given equivalent coefficient. The driving paths and running time of city buses were relatively fixed comparing with other vehicles, and their driving cycle can be predicted by route content. The proposed optimized strategy was applied on the factory sets of plug-in hybrid electric city bus.