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Demand for predictive powertrain control is rapidly increasing with the recent advancement of Advanced Driving Assistance Systems (ADAS) and Autonomous Driving (AD). The full or semi-autonomous functions could be leveraged to realize better user acceptance as well as powertrain efficiency of the connected vehicle utilizing the proposed Drive Horizon. The sensors of automated driving provide perception of surrounding driving environment which is required to safely navigate the vehicle in real-world driving scenarios. The proposed Drive Horizon provides real-time forecast of driving environment that a vehicle will encounter during its entire travel. This paper summarizes the vehicle’s future speed prediction technique which is an integral part of Drive Horizon for optimized energy control of the vehicle. The prediction model has been developed that integrates information from multiple sources including vehicle GPS, traffic information and map data.

The demand for clean energy efficient transportation is rapidly increasing to meet greenhouse gas emissions reduction and promote sustainability. Gasoline direct injection engines (GDI) have high thermal efficiency thus low greenhouse gas emissions compared with conventional port fueled engines. However, during cold start conditions GDI engines produce harmful emissions, including nitrogen oxides, hydrocarbons, and particulate matter. Thus, high pressure fuel system development for direct injection of gasoline is being conducted to reduce emissions during engine cold start. This paper summarized the effect of high fuel pressure and multiple injections on cold catalyst light-off strategy for rapid heating of the catalyst. Experiments were carried out using light-duty four-cylinder engine at cold catalyst light-off conditions with coolant temperature at 30 °C.