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Pollutant emissions are a major issue from all existing internal combustion engines, especially from Diesel engines, with nitrogen oxides (NOx) and particulate matter (PM) receiving particular attention. Simultaneous reduction of NOx and PM often presents a challenge to Diesel engine researchers because of the inherent trade-off between them due to their contradictory responses to oxygen content in a fuel. The use of liquid or gaseous biofuels in internal combustion engines, benefits in the reduction of life-cycle CO2 emissions, but suffers from unfavorable NOx/PM emissions trade-off. Hence, it is essential to moderate NOx/PM emissions trade-off in order to effectively use the Rapeseed Methyl Ester (RME) fuel as an alternative to the fossil Diesel fuel in Diesel engines.

The paper presents a comparative study of various models used to estimate gas dynamics in internal combustion engine (ICE) ducts. 1D models provide a sufficient accuracy, but they are still not implementable on current ECUs. On the other hand, low order models can be real-time but their lack of accuracy and high calibration cost are still a challenging problem. This work aims at presenting a comparison of currently used gas dynamics models to predict transient phenomena in engine ducts. It emphasizes on 1D and low order models. To test under engine-like conditions, the intake path of a virtual engine implemented in GT-Power and a production two cylinder engine are used. Results show a contrast in the performance of the different models, which gives the possibility to evaluate the various approaches. Based on this assessment and depending on the application in hand, the models can be chosen properly to estimate the gas dynamics in internal combustion engine ducts.

Compressor models play a major role as they define the boost pressure in the intake manifold. These models have to be suitable for real-time applications such as control and diagnosis and for that, they need to be both accurate and computationally inexpensive. However, the models available in the literature usually fulfill only one of these two competing requirements. On the one hand, physics-based models are often too complex to be evaluated on line. On the other hand, data-based models generally suffer insufficient extrapolation features. To combine the merits of these two types of models, this work presents an extended approach to compressor modeling with respect to thermo- and aerodynamic losses. In particular, the model developed by Martin et al. [1] is augmented to explicitly incorporate friction, incidence and heat transfer losses. The resulting model surpasses the extrapolation properties of data-based models and facilitates the generation of extended lookup tables.

Increasing the degrees of freedom in the air path has become a popular way to reduce the fuel consumption and pollutant emissions of modern combustion engines. That is why technical definitions will usually contain components such as multi or single-stage turbocharger, throttle, exhaust gas recirculation loops, wastegate, variable valve timing or phasing, etc. One of the biggest challenges is to precisely quantify the gas flows through the engine. They include fresh and burnt gases, with trapping and scavenging phenomena. An accurate prediction of these values leads to an efficient control of the engine air fuel ratio and torque. Fuel consumption and pollutant emissions are then minimized. In this paper, we propose to use an artificial neural network- based model as a prediction tool for the engine volumetric efficiency. Results are presented for a downsized turbocharged spark-ignited engine, equipped with inlet and outlet variable valve timing.

In Hybrid Electric Vehicles (HEV), the electrical hybridization offers different ways to reduce the fuel consumption: kinetic energy recuperation during vehicle deceleration, possibility of stopping the engine, and intelligent Energy Management System (EMS). Besides, with the future more stringent standards, there is a need to integrate the pollutant emissions in the EMS, since strictly reducing the fuel consumption can increase the emissions. The paper presents an optimal energy management strategy with constraints on pollutant emissions for gasoline-HEV, taking into account the 3-Way Catalyst Converter (3WCC). Based on a complete model of the powertrain, a mixed fuel consumption / pollutant emissions performance index is minimized with the Pontryaguin Minimum Principle (PMP) and two states, the battery State Of Charge and the 3WCC temperature.

In the past few years, the increasing market penetration of downsized engines has reduced the pollutant emissions of internal combustion engines. The addition of a turbocharger to the air path has usually enabled the dynamic performances of the vehicles to be maintained. However, in the development process, deciding on the appropriate set of components is not straightforward and a lengthy fitting process is usually required to find the right turbocharger. Car manufacturers usually have access to a limited library of compressors and turbines which have actually been built and for which measurement campaigns have been carried out. This study is motivated by the need to extend the libraries available for simulation in order to provide a substantial increase in freedom in the matching process.

Data maps are easy to put in place and require very low calculation time. As a consequence they are often valued over fully physic-based models. This is particularly true when it is question of turbochargers. However, even if these maps are directly provided by the manufacturer, they usually do not cover the entire engine operating range and are poorly discretized. That's why before implementing them into any model they need to be interpolated and extrapolated. This paper introduces a new interpolation/extrapolation method based on the idea of integrating more physics into the widespread Jensen and Kristensen's method [6]. It essentially relies on the turbo machinery equation analysis performed by Martin during his PhD thesis [9, 10, 11] and the interpolation and extrapolation strategies that he proposed. In most cases the new strategies presented in this paper rely on improvements of the models he proposed.

The objective of this paper is to present and to validate a numerical model of a single-cylinder pneumatic-combustion hybrid engine. The model presented in this paper contains 0-D sub-models for non-spatially distributed components: Engine cylinder, Air tank, wall heat losses. 1-D sub-models for spatially distributed components are applied on the compressive gas flows in pipes (intake, exhaust and charging). Each pipe is discretized, using the Two-Steps Lax-Wendroff scheme (LW2) including Davis T.V.D. The boundaries conditions used at pipe ends are Method Of Characteristics (MOC) based. In the specific case of a valve, an original intermediate volume MOC based boundary condition is used. The numerical results provided by the engine model are compared with the experimental data obtained from a single cylinder prototype hybrid engine on a test bench operating in 4-stroke pneumatic pump and 4 stroke pneumatic motor modes.

The energy management of a hybrid vehicle defines the vehicle power flow that minimizes fuel consumption and exhaust emissions. In a combined hybrid the complex architecture requires a multi-input control from the energy management. A classic optimal control obtained with dynamic programming shows that thanks to the high efficiency hybrid electric variable transmission, energy losses come mainly from the internal combustion engine. This paper therefore proposes a sub-optimal control based on the maximization of the engine efficiency that avoids multi-input control. This strategy achieves two aims: enhanced performances in terms of fuel economy and a reduction of computational time.

Nowadays, numerical modeling/simulation is an essential tool. It is used in all the design stages in order to improve quality and to reduce costs and time-to-market. As models address increasingly multiphysics and complex phenomena throughout the V-cycle process, modelers are confronted, sooner or later, with the need for model reduction. This paper describes the set-up of a virtual prototyping platform and highlights the interest of integrating energetic aspects, which are used to compute new model reduction criteria. A similar energy approach, “MORA” using “Activity”, is already used by Bond Graphists in order to obtain the “Proper Model” ([7] and [8]). The methodology presented here (PEMRA: “Power & Energy -based Model Reduction Algorithm”), with new power and energy criteria, makes it possible to obtain a simpler and more accurate reduced model than with MORA methodology, while improving the system's energy information.

This paper presents energy management strategies for a new hybrid pneumatic engine concept, which is specific by its configuration: It is not a vehicle but only an engine itself which is hybridized. This arrangement could provide as much as 30% of fuel saving depending on the driving cycle. Therefore different energy management strategies are proposed and compared in this paper. The first of them is called Causal Strategy and implements a rule-based control technique. A second strategy called Constant Penalty Coefficient is based on minimization of equivalent consumption, where the use of each energy source is formulated in a comparative unit. The balance between consumption of different energy source (chemical or pneumatic) is reached by introduction of an equivalence factor. The third strategy is called Variable Penalty Coefficient, where the equivalence factor is consider as variable within the amount of pneumatic energy stored in the air-tank.

Nowadays, (engine) downsizing using turbocharging appears as a major way for reducing fuel consumption. With this aim in view, the air actuators (throttle, Turbo WasteGate) control is needed for an efficient engine torque control especially to reduce pumping losses and to increase efficiency. This work proposes Nonlinear Model Predictive Control (NMPC) of the air actuators for turbocharged SI engines where the predictions are achieved by a neural model. The results obtained from a test bench of a Smart MCC engine show the real time applicability of the proposed method based on on-line linearization and the good control performances (good tracking, no overshoot) for various engine speeds.

To meet future pollutant emissions standards, it is crucial to be able to estimate the cycle by cycle composition of the combustion chamber charge. This charge consists of fresh air, fuel and residual gas from the previous cycle. Unfortunately, the residual gas fraction cannot be directly measured. Therefore, a model of residual gas fraction as a function of engine parameters and operating parameters has been developed. The model has been calibrated with exhaust pipe hydrocarbon measurements using a successive dilution method.

Torque based engine control seems to be the trend for the future for powertrain management (automatic gearbox, hybrid vehicles). Today, torque estimation is best achieved using cylinder pressure transducers. This paper proposes a method to achieve a good accuracy of the torque using Bézier curves to reconstruct the cylinder pressure peak from the low frequency embedded pressure measurements. As is, IMEP cannot be used on a cycle to cycle basis for engine torque control, due to the very high cycle to cycle variability of SI engines. To improve the quality of the IMEP feedback data, this paper proposes a moving horizon filtering method.

The optimization of fuel efficiency and the minimization of the residual gas fraction require individual cylinder control of the amounts of inducted air mass and injected fuel mass. Determination of an individual cylinder air/fuel ratio (AFR) regulator is based on the measured AFR for each cylinder, using 4 proportional UEGO sensors. The innovative character of this study describes a unified and robust individual cylinder AFR estimator, using a single measuring point: a proportional oxygen sensor located in the exhaust manifold. The model used for the estimator is a state model such that the dimension of the state and measurement matrices are unique, whatever the manifold configuration and the sensor position (confluence point or exhaust manifold: unified model), the engine speed (robust model).