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Increasingly restrictive emission standards and CO2 targets drive the need for innovative engine architectures that satisfy the design constraints in terms of performance, emissions and drivability. Downsizing is one major trend for Spark-Ignition (SI) engines. For downsized SI engines, the increased boost levels and compression ratios may lead to a higher propensity of abnormal combustions. Thus increased levels of Exhaust Gas Recirculation (EGR) are used in order to limit the appearance of knock and super-knock. The drawback of high EGR rates is the increased tendency for Cycle-to-Cycle Variations (CCV) it engenders. A possible way to reduce CCV could be the generation of an increased in-cylinder turbulence to accelerate the combustion process. To manage all these aspects, 1D simulators are increasingly used. Accordingly, adapted modeling approaches must be developed to deal with all the relevant physics impacting combustion and pollutant emissions formation.

Fuel consumption is an essential factor that requires to be minimized in the design of a vehicle powertrain. Simple energy models can be of great help - by clarifying the role of powertrain dimensioning parameters and reducing the computation time of complex routines aiming at optimizing these parameters. In this paper, a Fully Analytical fuel Consumption Estimation (FACE) is developed based on a novel GRaphical-Analysis-Based fuel Energy Consumption Optimization (GRAB-ECO), both of which predict the fuel consumption of light- and heavy-duty series hybrid-electric powertrains that is minimized by an optimal control technique. When a drive cycle and dimensioning parameters (e.g. vehicle road load, as well as rated power, torque, volume of engine, motor/generators, and battery) are considered as inputs, FACE predicts the minimal fuel consumption in closed form, whereas GRAB-ECO minimizes fuel consumption via a graphical analysis of vehicle optimal operating modes.

CNG is one of the most promising alternate fuels for passenger car applications. CNG is affordable, is available worldwide and has good intrinsic properties including high knock resistance and low carbon content. Usually, CNG engines are developed by integrating CNG injectors in the intake manifold of a baseline gasoline engine, thereby remaining gasoline compliant. However, this does not lead to a bi-fuel engine but instead to a compromised solution for both Gasoline and CNG operation. The aim of the study was to evaluate the potential of a direct injection spark ignition engine derived from a diesel engine core and dedicated to CNG combustion. The main modification was the new design of the cylinder head and the piston crown to optimize the combustion velocity thanks to a high tumble level and good mixing. This work was done through computations. First, a 3D model was developed for the CFD simulation of CNG direct injection.

This paper addresses the robustness of an optimal online energy management for diesel hybrid electric vehicle (HEV). Optimal strategy is based on the Equivalent Consumption Minimization Strategy (ECMS). Optimal torque split between engine and electric motor is found by minimizing fuel consumption and Nitrogen Oxides (NOx) emissions. Online adaptation is made in order to ensure battery charge sustainability and good driveability when driving conditions are unknown. The strategy is tested in simulation over one hundred driving cycles representative of real-world conditions. Results obtained with the online strategy are compared with those of an offline optimal strategy (knowing the driving cycle a priori). Even if a slight degradation is noticed in comparison to optimal case, fuel economy and NOx reduction - provided by hybridization - are conserved with the online strategy.

Meeting Euro 6d NOx emission regulations lower than 80 mg/km for light duty diesel (60 mg/km gasoline) vehicles remains a challenge, especially during cold-start tests at which the selective catalyst reduction (SCR) system does not work because of low exhaust gas temperatures (light-off temperature around 200 °C). While several exhaust aftertreatment system (EATS) designs are suggested in literature, solutions with gaseous ammonia injections seem to be an efficient and cost-effective way to enhance the NOx abatement at low temperature. Compared to standard SCR systems using urea water solution (UWS) injection, gaseous NH3 systems allow an earlier injection, prevent deposit formation and increase the NH3 content density. However non-uniform ammonia mixture distribution upstream of the SCR catalyst remains an issue. These exhaust gas/ NH3 inhomogeneities lead to a non-optimal NOx reduction performance, resulting in higher than expected NOx emissions and/or ammonia slip.

In this paper, a new physics-based model for the prediction of NOx emissions produced by diesel engines is presented. The aim of this work is to provide a reference model for the validation of control strategies and NOx estimators. The model describes the NOx production in the burned gas zone where the burned gas temperature sub-model is adapted to be generic and tunable. The model consists of three main sub-models for the estimation of the burned gas temperature, the concentration of the species in the burned gases and the NOx formation, respectively. A new model for estimating the burned gas temperature, known to have a strong impact on thermal NOx formation rate, is proposed. The model depends on the intake burned gas ratio and the combustion phasing computed from the cylinder pressure. This model has a limited number of calibration parameters identified so that NOx model output matches with experimental data measured in a four-cylinder, four-stroke, direct-injection diesel engine.

The present paper is concerned with part of the work performed by Renault, IFPEN and Politecnico di Torino within a research project founded by the European Commission. The project has been focused on the development of a dedicated CNG engine featuring a 25% decrease in fuel consumption with respect to an equivalent Diesel engine with the same performance targets. To that end, different technologies were implemented and optimized in the engine, namely, direct injection, variable valve timing, LP EGR with advanced turbocharging, and diluted combustion. With specific reference to diluted combustion, it is rather well established for gasoline engines whereas it still poses several critical issues for CNG ones, mainly due to the lower exhaust temperatures. Moreover, dilution is accompanied by a decrease in the laminar burning speed of the unburned mixture and this generally leads to a detriment in combustion efficiency and stability.

IFP Energies nouvelles (IFPEN) has a large experience in the development of engine simulation platforms. During the last decade, the Dual Flame Model (DFM), a physical 0-dimensional (0D) combustion model designed for Diesel applications, was developed and continuously improved. The DFM formalism allows to represent quite precisely the in-cylinder combustion process scenario, by accounting for the first order relevant physics impacting fuel oxidation. First of all, this allows to account for the impact of engine actuators on combustion (e.g. injection systems performing complex injection strategies, Low Pressure and High Pressure EGR loops,…) and then to describe the pollutant emissions formation processes, being chemical kinetics strongly dependent on the in-cylinder thermochemical conditions. The aim of this communication is to present the potential of using the DFM model in the different stages of a Diesel engine development process for pollutant emissions optimization.

An automatic mesh generation process for a body fitted 3D CFD code is presented in this paper along with the methodology to guarantee the mesh quality. This tool named OMEGA (Optimized MEsh Generation Automation) uses a direct coupling procedure between the IFP-C3D solver and a hybrid mesher Centaur. Thanks to this automatic procedure, the engineering time needed for body fitted 3D CFD simulation in internal combustion engines is drastically reduced from a few weeks to a few hours. Valve and piston motion laws are just given as input files and geometries and meshes are automatically moved and generated. Unlike other procedures, this automatic mesh generation does not use an intermediate geometry discretization (STL file, tetrahedral surface mesh) but directly the original CAD that has been modified thanks to the geometry motion functionalities integrated into the mesher.

The objective of this paper is to present the results of the GT Power calibration with engine test results of the air loop system technology down selection described in the SAE Paper No. 2012-01-0831. Two specific boosting systems were identified as the preferred path forward: (1) Super-turbo with two speed Roots type supercharger, (2) Super-turbo with centrifugal mechanical compressor and CVT transmission both downstream a Fixed Geometry Turbine. The initial performance validation of the boosting hardware in the gas stand and the calibration of the GT Power model developed is described. The calibration leverages data coming from the tests on a 2 cylinder 2-stroke 0.73L diesel engine. The initial flow bench results suggested the need for a revision of the turbo matching due to the big gap in performance between predicted maps and real data. This activity was performed using Honeywell turbocharger solutions spacing from fixed geometry waste gate to variable nozzle turbo (VNT).

With the increase of heavy-duty transportation, more fuel efficient technologies and services have become of great importance due to their environmental and economical impacts for the fleet managers. In this paper, we first develop a new analytical model of the heavy-truck for its dynamics and its fuel consumption, and valid the model with experimental measurements. Then, we propose a bi-level optimization approach to reduce the fuel consumption, thus the CO2 emissions, while ensuring several safety constraints in real-time. Numerical results show that important reduction of the fuel consumption can be achieved, while satisfying imposed safety constraints.

Accurately predicting the future behavior of the surrounding traffic, especially the velocity of the lead vehicle is important for optimizing the energy consumption and improve the safety of Connected and Automated Vehicles (CAVs). Several studies report methods to predict short-to-mid-length lead vehicle velocity using stochastic models or other data-driven techniques, which require availability of extensive data and/or Vehicle-to-Vehicle (V2V) communication. In the absence of connectivity, or in data-restricted cases, the prediction must rely only on the measured position and relative velocity of the lead vehicle at the current time. This paper proposes two velocity predictors to predict short-to-mid-length lead vehicle velocity. The first predictor is based on a Constant Acceleration (CA) with an augmented stop mode. The second one is based on a modified Enhanced Driver Model (EDM-LOS) with line-of-sight feature.