Development of a control-oriented physical model for cylinder pressure peak estimation in SI engines

Abstract

Powertrain electrification is currently considered a promising solution to meet the challenge of CO2 reduction requested by future emission regulations for the automotive industry. Despite the potential of full electric powertrains, such as Battery Electric Vehicles (BEVs) and Fuel Cell Electric Vehicles (FCEVs), their diffusion has been severely limited by various technological aspects, market drivers and policies. In this scenario, there is a growing interest in Hybrid Electric Vehicles (HEVs) powered by spark-ignited Dedicated Hybrid Engines (DHEs), mainly because of their high efficiency and very-low pollutants. However, since DHEs are usually operated at relatively high loads, with advanced combustions and high in-cylinder pressure and temperature peaks, reliability over time becomes a crucial aspect to be guaranteed by the engine management systems. This work presents development and validation of an innovative control-oriented model, suitable to predict the maximum in-cylinder pressure of SI engines. The procedure is based on information that can be measured or estimated, in real time, on-board a vehicle, and the computational cost is compatible with modern engine control units. To verify accuracy and robustness of the methodology, two different SI engines have been analyzed over their whole operating range: a turbocharged Gasoline Direct Injection (GDI) engine and a Naturally Aspirated (NA) engine. After calibrating the model parameters using both average and cycle-by-cycle data, the accuracy of the maximum in-cylinder pressure estimation has been evaluated always returning errors lower than 3% between measured and estimated maximum in-cylinder pressure.