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Modern turbo-charged downsized engines reach high values of specific power, causing a significant increase of the exhaust gas temperature. Such parameter plays a key role in the overall powertrain environmental impact because it strongly affects both the catalyst efficiency and the turbine durability. In fact, common techniques to properly manage the turbine inlet gas temperature are based on mixture enrichment, which causes both a steep increase in specific fuel consumption and a decrease of catalyst efficiency. At the test bench, exhaust gas temperature is typically measured using thermocouples that are not available for on-board application, and such information is processed to calibrate open-loop look-up-tables. A real-time, reliable, and accurate exhaust temperature model would then represent a strategic tool for improving the performance of the engine control system.

Due to the ever increasingly stringent emission regulations for passenger vehicles, the efficiency and performance increase of Spark Ignition (SI) engines have been under the focus of the engine manufacturers. The quest for efficiency and performance increase has led to the development of increasingly complex powertrains and control strategies. The development process requires novel methods that feature a smooth transition between the real and the virtual prototypes. Furthermore, to reduce the development time and cost, developing an engine simulator with a low computational effort and good accuracy, which predicts the engine behavior on the entire operating range, plays a crucial role. This work proposes an Artificial Intelligence-based engine simulator for a Spark Ignition engine. The simulator relies on Neural Networks for the calculation of the main combustion metrics. In the first part of this paper, the data acquired at the engine test cell are analyzed.

The challenge of industrial carbon footprint reduction is led by the engine manufacturers that are developing new technologies and fuels to lower CO2 emissions. Although the deployment of relevant investments for the development of battery electric vehicles, diesel, and gasoline cars are still widely used, especially for their longer operating range, faster refueling, and lower cost. For this reason, more efficient traditional internal combustion engines can guide the transition towards new propulsion systems. In this document, the innovative piston damage and exhaust gas temperature models previously developed by the authors are reversed and coupled to manage the combustion process, increasing the overall energy conversion efficiency. The instantaneous piston erosion and the exhaust gas temperature at the turbine inlet are evaluated according to the models’ estimation which manages both the spark advance, and the target lambda.