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Partially Premixed Combustion (PPC) has shown to be a promising advanced combustion mode for future engines in terms of efficiency and emission levels. The combustion timing should be suitably phased to realize high efficiency. However, a simple constant model based predictive controller is not sufficient for controlling the combustion during transient operation. This article proposed one learning based model predictive control (LBMPC) approach to achieve controllability and feasibility. A learning model was developed to capture combustion variation. Since PPC engines could have unacceptably high pressure-rise rates at different operation points, triple injection is applied as a solvent, with the use of two pilot fuel injections. The LBMPC controller utilizes the main injection timing to manage the combustion timing. The cylinder pressure is used as the combustion feedback. The method is validated in a multi-cylinder heavy-duty PPC engine for transient control.

Powertrain efficiency is a critical factor in lowering fuel consumption and reducing the emission of greenhouse gases for an internal combustion engine. One method to increase the powertrain efficiency is to recover some of the wasted heat from the engine using a waste heat recovery system e.g. an organic Rankine cycle. Most waste heat recovery systems in use today for combustion engines use the waste heat from the exhaust gases due to the high temperatures and hence, high energy quality. However, the coolant represents a major source of waste heat in the engine that is mostly overlooked due to its lower temperature. This paper studies the potential of using elevated coolant temperatures in internal combustion engines to improve the viability of low temperature waste heat recovery.