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New methodologies have been developed to optimize EGR rate and injection timing in diesel engines, with the aim of minimizing fuel consumption (FC) and NOx engine-out emissions. The approach entails the application of a recently developed control-oriented engine model, which includes the simulation of the heat release rate, of the in-cylinder pressure and brake torque, as well as of the NOx emission levels. The engine model was coupled with a C-class vehicle model, in order to derive the engine speed and torque demand for several driving cycles, including the NEDC, FTP, AUDC, ARDC and AMDC. The optimization process was based on the minimization of a target function, which takes into account FC and NOx emission levels. The selected control variables of the problem are the injection timing of the main pulse and the position of the EGR valve, which have been considered as the most influential engine parameters on both fuel consumption and NOx emissions.

A real-time approach has been developed and assessed to control BMEP (brake mean effective pressure) and MFB50 (crank angle at which 50% of fuel mass has burnt) in a Euro 6 1.6L GM diesel engine. The approach is based on the use of feed-forward ANNs (artificial neural networks), which have been trained using virtual tests simulated by a previously developed low-throughput physical engine model. The latter is capable of predicting the heat release and the in-cylinder pressure, as well as the related metrics (MFB50, IMEP - indicated mean effective pressure) on the basis of an improved version of the accumulated fuel mass approach. BMEP is obtained from IMEP taking into account friction losses. The low-throughput physical model does not require high calibration effort and is also suitable for control-oriented applications. However, control tasks characterized by stricter demands in terms of computational time may require a modeling approach characterized by a further lower throughput.

A new predictive zero-dimensional low-throughput combustion model has been applied to both PCCI (Premixed Charge Compression Ignition) and conventional diesel engines to simulate HRR (Heat Release Rate) and in-cylinder pressure traces on the basis of the injection rate. The model enables one to estimate the injection rate profile by means of the injection parameters that are available from the engine ECU (Electronic Control Unit), i.e., SOI (Start Of main Injection), ET (Energizing Time), DT (Dwell Time) and injected fuel quantities, taking the injector NOD (Nozzle Opening Delay) and NCD (Nozzle Closure Delay) into account. An accumulated fuel mass approach has been applied to estimate Qch (released chemical energy), from which the main combustion parameters that are of interest for combustion control in IC engines, such as, SOC (Start Of Combustion), MFB50 (50% of Mass Fraction Burned) have been derived.

An unsupervised machine-learning technique, aimed at the identification of the optimal rule-based control strategy, has been developed for parallel hybrid electric vehicles that feature a torque-coupling (TC) device, a speed-coupling (SC) device or a dual-mode system, which is able to realize both actions. The approach is based on the preliminary identification of the optimal control strategy, which is carried out by means of a benchmark optimizer, based on the deterministic dynamic programming technique, for different driving scenarios. The optimization is carried out by selecting the optimal values of the control variables (i.e., transmission gear and power flow) in order to minimize fuel consumption, while taking into account several constraints in terms of NOx emissions, battery state of charge and battery life consumption.