In the last few years, the automotive industry had to face three main\ud
challenges: compliance with more severe pollutant emission limits,\ud
better engine performance in terms of torque and drivability and\ud
simultaneous demand for a significant reduction in fuel consumption.\ud
These conflicting goals have driven the evolution of automotive\ud
engines. In particular, the achievement of these mandatory aims,\ud
together with the increasingly stringent requirements for carbon\ud
dioxide reduction, led to the development of highly complex engine\ud
architectures needed to perform advanced operating strategies.\ud
Therefore, Variable Valve Actuation (VVA), Exhaust Gas Recirculation\ud
(EGR), Gasoline Direct Injection (GDI), turbocharging, powertrain\ud
hybridization and other solutions have gradually and widely been\ud
introduced into modern internal combustion engines, enhancing the\ud
possibilities of achieving the required goals. However, none of the\ud
improvements would have been possible without the contextual\ud
development of electronics. In fact, that solutions have highly increased\ud
the complexity of engine control and management because of the\ud
degrees of freedom available for the engine regulation, thus resulting in\ud
a long calibration time. In particular, base calibration is the most\ud
onerous phase of the engine control, both in terms of experimental and\ud
computational effort and costs. This paper addresses some critical\ud
issues concerning the calibration of control parameters through the use\ud
of a specific Model-Based Computer Aided Calibration algorithm\ud
developed by the authors to automate the calibration process and\ud
minimize calibration errors. The proposed methodology is also based\ud
on the use of neural networks (NN). In particular, starting from a\ud
reduced number of experimental data, NN provide a detailed engine\ud
data sheets used as input to the actual calibration process itself. The\ud
proposed algorithm provides optimal portability and reduced\ud
calibration time. The research also highlights how the developed\ud
methodology could be useful to identify possible enhancements for\ud
specific ECU engine models that can improve the accuracy of the\ud
calibration process by using more detailed physically based functions.\ud
The results of the proposed research clearly highlight how, in engine\ud
control, more accurate physical modeling may lead to promising results\ud
and better performance, ultimately enhancing the accuracy, time,\ud
experimental effort and cost savings of the calibration process
The remarkable investments made by manufacturers over the last few decades have contributed to improving the performance of internal combustion engines in every aspect: lower polluting emissions, greater specific power and thermal efficiency. Despite this, on an average, about 40% of the thermal power theoretically available from the combustion of the fuel is still stored in the exhaust gases and therefore dispersed in the environment. In this work the modeling and validation of a waste heat recovery (WHR) plant will be described, combining the engine with a low temperature Organic Rankine Cycle (ORC) system, in order to investigate the feasibility of this system on board of a vehicle, analyzing the quantity of thermal power recovered and made available in the form of electrical power. The ORC plant is modeled using a 0D/1D thermo-fluid dynamic approach. Starting from experimental tests, a map-based model for the piston pump and the scroll expander has been developed. The model has been validated through the use of a vector optimization technique, exploiting a genetic algorithm (MOGA). Subsequently, this system has been coupled to a spark ignition engine for automotive applications, adapting its speed range to comply with the ORC experimental tests. To have an accurate control over the expander inlet temperature, a bypass circuit and two throttles actuated by a PI controller have been implemented. The simulations were performed by considering 18 engine points at maximum load and different rpm. An average thermal efficiency increase of the system of 2.6% was obtained by introducing the recovery plant, and wide improvement chance can be foreseen in the case of ORC full-power use.
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