1,721,058 research outputs found
Development and Identification of a Hierarchical System of Models for Rapid Prototyping of SI Engines
No full textEstimation of Air-Fuel Ratio and Cylinder Wall Temperature from Pressure Cycle in s.i. Automotive Engines
No full textTwo techniques for the estimation of both Air-Fuel (A/F) ratio and cylinder wall temperature (Tw), based on the measurement of in-cylinder pressure cycle, are presented.
For the Air-Fuel ratio the results obtained by the approach originally proposed by Gassenfeit, Powell and Patrick for the evaluation of in-cylinder A/F ratio by using measured pressure cycle statistical moments are discussed.
The detection of chamber wall temperature is based on the identification of the inversion point for the net heat flux between cylinder wall and gas mixture during compression stroke (i.e. local adiabaticity condition). In such conditions, the mean wall temperature can be assumed equal to the mean gas mixture temperature which in turn is computed from thermodynamic relationships.
Both techniques have been applied on a wide range of experimental data during steady-state operating conditions and for some transient engine manoeuvres with satisfactory level of precision
A Model for the Dynamic Simulation of Hybrid Vehicles with Pem Fuel Cell
No full textThe paper focuses on the development of a dynamic model for a hybrid vehicle equipped with a PEM fuel cell and a battery pack. An integrated structure of mathematical models has been implemented, whose development is based on previous studies carried out by the research group on the modeling of energy conversion systems for conventional and hybrid vehicles. The whole model simulates real driving cycles aiming at providing the data needed for selecting the best control strategies in terms of performance and fuel economy. A comparison with thermal-hybrid vehicles and conventional systems performance is presented together with an energy analysis carried out for both the single stack and its auxiliaries and the overall system (vehicle
Experimental Validation of a Recurrent Neural Network for Air-Fuel Ratio Dynamic Simulation in S.I. I.C. Engines
No full textThe paper deals with the simulation of the wall wetting dynamics in SI engines, making use of Recurrent Neural Networks (RNN). RNN are derived from the Multi Layer Feedforward Neural Networks, largely adopted for static mapping, by considering feedback connections between output and input layers. A Multi Input-Single Output structure has been adopted, assuming injected fuel, manifold pressure and engine speed as external input variables; the Air-Fuel Ratio at the exhaust gas oxygen sensor location has been considered as system output. The RNN has been trained (i.e. identified) and tested vs. a set of transient data measured on a commercial 4 cylinders SI engine at the test bench. The results show a good level of accuracy confirming the suitability of RNN for both HIL simulation or off-line identification of classical Mean Value Models with a drastic reduction of the calibration effort
Optimal energy management for hybrid electric vehicles based on dynamic programming and receding horizon
No full textFuel consumption and emissions in parallel hybrid electric vehicles (HEVs) are directly linked to the way the load request to the wheels is managed between the internal combustion engine and the electric motor powered by the battery. A significant reduction in both consumption and emissions can be achieved by optimally controlling the power split on an entire driving mission (full horizon—FH). However, the entire driving path is often not predictable in real applications, hindering the fulfillment of the advantages gained through such an approach. An improvement can be achieved by exploiting more information available onboard, such as those derived from Advanced Driver Assistance Systems (ADAS) and vehicle connectivity (V2X). With this aim, the present work presents the design and verification, in a simulated environment, of an optimized controller for HEVs energy management, based on dynamic programming (DP) and receding horizon (RH) approaches. The control algorithm entails the partial knowledge of the driving mission, and its performance is assessed by evaluating fuel consumption related to a Worldwide harmonized Light vehicles Test Cycle (WLTC) under different control features (i.e., horizon length and update distance). The obtained results show a fuel consumption reduction comparable to that of the FH, with maximum drift from optimal consumption of less than 10%
A Neural Network Air-Fuel Ratio Estimator for Control and Diagnostics in Spark-Ignited Engines
No full text
Development of Recurrent Neural Networks for Virtual Sensing of NOx Emissions in Internal Combustion Engines
No full text- …
