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Methodological Approach for 1D Simulation of Port Water Injection for Knock Mitigation in a Turbocharged DISI Engine

: In the upcoming years, more challenging CO2 emission targets along with the introduction of more severe Real Driving Emissions limits are expected to foster the development and the exploitation of innovative technologies to further improve the efficiency of automotive Spark Ignition (SI) engines. Among these technologies, Water Injection (WI), thanks to its knock mitigation capabilities, can represent a valuable solution, although it may significantly increase the complexity of engine design and calibration. Since, to tackle such a complexity, reliable virtual development tools seem to be mandatory, this paper aims to describe a quasi-dimensional approach to model a Port Water Injection (PWI) system integrated in a Turbocharged Direct Injection Spark Ignition (T-DISI) engine. Through a port-puddling model calibrated with 3D-CFD data, the proposed methodology was proven to be able to properly replicate transient phenomena of water wall film formation, catching cycle by cycle the amount of water that enters into the cylinder and is therefore available for knock mitigation. Moreover, when compared with experimental measurements under steady state operating conditions, this method showed good capabilities to predict the impact of the water content on the combustion process and on the knock occurrence likelihood

Modelling of combustion and knock onset risk in a high-performance turbulent jet ignition engine

The reduction of CO2 emissions, and hence of fuel consumption, is currently a key driver for the development of innovative SI engines for passenger car applications. In recent years, motorsport technical regulations in the highest categories have seen the introduction of limits concerning the fuel flow rate and the total amount of fuel per race, thus driving engine development toward further reduction of specific fuel consumption. Among the different techniques that can be shared between conventional and high-performance SI engines, turbocharging, compression ratio increase and Turbulent Jet Ignition (TJI) have shown a significant potential for fuel consumption reduction. The combination of turbocharging and compression ratio increase, however, can promote the onset of knocking combustion, with detrimental effects on engine’s efficiency and durability. Additionally, engines equipped with TJI systems show unusual combustion development and knock onset. In this study a methodology for the 3D-CFD modelling of combustion and knock onset risk was developed for a high-performance turbocharged engine featuring a passive TJI system. First, a comprehensive numerical study was carried out in a commercially available software, CONVERGE 2.4, in order to develop a 3D-CFD model able to reproduce the available experimental data. The resulting 3D-CFD model was then validated on different working conditions featuring different spark advances. Lastly, a methodology for the assessment of knock onset risk was developed, which led to the definition of two novel knock-risk indexes based on the progress of chemical reactions within the combustion chamber. The proposed knock-risk indexes showed good agreement with the experimental data

Real World Operation of a Complex Plug-in Hybrid Electric Vehicle: Analysis of Its CO2 Emissions and Operating Costs

Plug-in hybrid electric vehicles (pHEVs) could represent the stepping stone to move towards a more sustainable mobility and combine the benefits of electric powertrains with the high range capability of conventional vehicles. Nevertheless, despite the huge potential in terms of CO2 emissions reduction, the performance of such vehicles has to be deeply investigated in real world driving conditions considering also the CO2 production related to battery recharge which, on the contrary, is currently only partially considered by the European regulation to foster the diffusion of pHEVs. Therefore, this paper aims to assess, through numerical simulation, the real performance of a test case pHEV, the energy management system (EMS) of which is targeted to the minimization of its overall CO2 emissions. The paper highlights, at the same time, the relevance of the CO2 production related to the battery recharge from the power grid. Different technologies mixes used to produce the electricity required for the battery recharge are also taken into account in order to assess the influence of this parameter on the vehicle CO2 emissions. Finally, since the operating cost still represents the main driver in orienting the customer’s choice, an alternative approach for the EMS, targeted to the minimization of this variable, is also analyzed

Real CO2 emissions benefits and end user’s operating costs of a plug-in Hybrid Electric Vehicle

AbstractAlthough plug-in Hybrid Electric Vehicles (pHEVs) can be considered a powerful technology to promote the change from conventional mobility to e-mobility, their real benefits, in terms of CO2 emissions, depend to a great extent on the average efficiency of their Internal Combustion Engine and on the energy source mix which is used to supply the electrical demand of pHEV.Furthermore the operating cost of the vehicle should also be taken into account in the design process, since it represents the main driver in the customer’s choice.This article has the purpose of assessing, through numerical simulations, the effects of different technology mixes used to produce electrical energy for the battery recharging, of different Internal Combustion Engines on the pHEV performance, and highlighting the main differences with respect to the regulatory test procedure

Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario

In a context in which the connectivity level of last-generation vehicles is constantly onthe rise, the combined use of Vehicle-To-Everything (V2X) connectivity and autonomous drivingcan provide remarkable benefits through the synergistic optimization of the route and the speedtrajectory. In this framework, this paper focuses on vehicle ecodriving optimization in a connectedenvironment: the virtual test rig of a premium segment passenger car was used for generatingthe simulation scenarios and to assess the benefits, in terms of energy and time savings, that theintroduction of V2X communication, integrated with cloud computing, can have in a real-worldscenario. The Reference Scenario is a predefined Real Driving Emissions (RDE) compliant route,while the simulation scenarios were generated by assuming two different penetration levels of V2Xtechnologies. The associated energy minimization problem was formulated and solved by means of aVariable Grid Dynamic Programming (VGDP), that modifying the variable state search grid on thebasis of the V2X information allows to drastically reduce the DP computation burden by more than95%. The simulations show that introducing a smart infrastructure along with optimizing the vehiclespeed in a real-world urban route can potentially reduce the required energy by 54% while shorteningthe travel time by 38%. Finally, a sensitivity analysis was performed on the biobjective optimizationcost function to find a set of Pareto optimal solutions, between energy and travel time minimization

Development of a neural network-based energy management system for a plug-in hybrid electric vehicle

The high potential of Artificial Intelligence (AI) techniques for effectively solving complex parameterization tasks also makes them extremely attractive for the design of the Energy Management Systems (EMS) of Hybrid Electric Vehicles (HEVs). In this framework, this paper aims to design an EMS through the exploitation of deep learning techniques, which allow high non-linear relationships among the data characterizing the problem to be described. In particular, the deep learning model was designed employing two different Recurrent Neural Networks (RNNs). First, a previously developed digital twin of a state-of-the-art plug-in HEV was used to generate a wide portfolio of Real Driving Emissions (RDE) compliant vehicle missions and traffic scenarios. Then, the AI models were trained off-line to achieve CO2 emissions minimization providing the optimal solutions given by a global optimization control algorithm, namely Dynamic Programming (DP). The proposed methodology has been tested on a virtual test rig and it has been proven capable of achieving significant improvements in terms of fuel economy for both charge-sustaining and charge-depleting strategies, with reductions of about 4% and 5% respectively if compared to the baseline Rule-Based (RB) strategy

Large Eddy Simulations (LES) towards a comprehensive understanding of Ducted Fuel Injection concept in non-reacting conditions

The diesel combustion research is increasingly focused on Ducted Fuel Injection (DFI), a promising concept to abate engine-out soot emissions in Compression-Ignition engines. A large set of experiments and numerical simulations, at medium-low computational cost, showed that the duct adop- tion in front of the injector nozzle activates several soot mitigation mechanisms, leading to quasi-zero soot formation in several engine-like operating conditions. However, although the simplified CFD mod- elling so far played a crucial role for the preliminary understanding of DFI technology, a more accurate turbulence description approach, combined with a large set of numerical experiments for statistical pur- poses, is of paramount importance for a robust knowledge on the DFI physical behavior. In this context, the present work exploits the potential of Large Eddy Simulations (LES) to analyze the non-reacting spray of DFI configuration compared with the unconstrained spray. For this purpose, a previously developed spray model, calibrated and validated in the RANS framework against an exten- sive amount of experimental data related to both free spray and DFI, has been employed. This high- fidelity simulation model has been adapted for LES, firstly selecting the best grid settings, and then carrying out several numerical experiments for both spray configurations until achieving a satisfying statistical convergence. With this aim, the number of independent samples for the averaging procedure has been increased exploiting the axial symmetry characteristics of the present case study. The relia- bility of this methodology has been herein proven, highlighting an impressive runtime saving without any remarkable worsening of the accuracy level. Thanks to this approach, a detailed description of the main DFI-enabled soot mitigation mechanisms has been achieved, bridging the still open knowledge gap in the physical understanding of the impact of spray-duct interaction