Air-Fuel Ratio Control of Spark Ignition Engines With Unknown System Dynamics Estimator: Theory and Experiments

This brief addresses the emission reduction of spark ignition engines by proposing a new control to regulate the air-fuel ratio (AFR) around the ideal value. After revisiting the engine dynamics, the AFR regulation is represented as a tracking control of the injected fuel amount. This allows to take the fuel film dynamics into consideration and simplify the control design. The lumped unknown engine dynamics in the new formulation are online estimated by suggesting a new effective unknown system dynamics estimator. The estimated variable can be superimposed on a commercially configured, well-calibrated gain scheduling like proportional-integral-differential (PID) control to achieve a better AFR response. The salient feature of this proposed control scheme lies in its simplicity and the small number of required measurements, that is, only the air mass flow rate, the pressure and temperature in the intake manifold, and the measured AFR value are used. Practical experiments on a Tata Motors Limited two-cylinder gasoline engine are carried out under a realistic driving cycle. The comparative results show that the proposed control can achieve an improved AFR control response and reduced emissions

Observer Based Cylinder Charge Estimation for Spark-ignition Engines

Internal combustion engines require accurate cylinder charge estimation for determining engine torque, controlling air-to-fuel ratio (AFR), and ensuring high after-treatment efficiency. This is challenging due to the highly transient operating conditions that are common in automobile engines. The problem is further complicated by spark ignition (SI) engine technologies such as variable valve timing (VVT) and exhaust gas recirculation (EGR) which are applied to improve fuel economy and reduce pollutant emissions. With manifold filling/emptying/mixing phenomenon and different actuator response times, these technologies significantly increase the complexity of cylinder charge estimation. Current cylinder charge estimation methodologies require a combination of sensors and empirical models to deal with the high degrees of control freedom existent on the engine. But these methods have the drawbacks of great dependency on accurate calibration and poor transient performance. Most importantly, the current methods isolate feed-forward cylinder charge estimation and feedback AFR control. When there is discrepancy between target lambda value and sensed lambda value at exhaust side, the current control/estimation method will trim the fuel injection amount no matter where the error source is. As a matter of fact, the error might come from the throttle flow estimation, the fuel injection flow estimation, EGR flow estimation, or any combination of these error sources. Increased air-path complexity and drawbacks of traditional methods drive the need for cost effective solutions that produce high air/EGR/fuel charge estimation accuracy with the ability to identify the error source while minimizing sensor cost, computational effort, and calibration time. This research first evaluates the existing work on air charge estimation for SI engines with massive experimental tests covering various operating conditions, which are designed for the algorithm verification of this research. Then several estimation methods which utilize both Manifold Absolute Pressure (MAP) and Mass Air Flow (MAF) sensors are studied and analyzed. Reduction of calibration effort and improvement of accuracy are observed from the proposed cylinder air charge estimation methods. Following that, a model is built to study the engine gas path dynamics and characteristics and then simplified to provide system dynamic basis for the following estimation algorithm development. Using the developed model, a disturbance observer based cylinder charge estimation technique is developed based on a combination of sensors including MAF, MAP, and exhaust lambda sensors. This developed algorithm significantly improves engine states estimation accuracy compared to conventional Single-Input-Single-Output (SISO) methods. Also, the augmentation of disturbance observation is able to pin point the source of the estimation error. Through experimental validation, using the developed estimation method with proper parameters, the error source of estimation can be identified and rectified when disturbance is introduced to throttle flow model, EGR flow model, fuel injection flow model or any combination of these models. The structure of the proposed algorithm should adapt to most SI engine configurations. It can help the engine controller to mitigate modeling errors thus improve the performance of physics model based engine control especially AFR control

IN-CYLINDER MASS FLOW ESTIMATION AND MANIFOLD PRESSURE DYNAMICS FOR STATE PREDICTION IN SI ENGINES

The aim of this paper is to present a simple model of the intake manifold dynamics of a spark ignition (SI) engine and its possible application for estimation and control purposes. We focus on pressure dynamics, which may be regarded as the foundation for estimating future states and for designing model predictive control strategies suitable for maintaining the desired air fuel ratio (AFR). The flow rate measured at the inlet of the intake manifold and the in-cylinder flow estimation are considered as parts of the proposed model. In-cylinder flow estimation is crucial for engine control, where an accurate amount of aspired air forms the basis for computing the manipulated variables. The solutions presented here are based on the mean value engine model (MVEM) approach, using the speed-density method. The proposed in-cylinder flow estimation method is compared to measured values in an experimental setting, while one-step-ahead prediction is illustrated using simulation results

Review of air fuel ratio prediction and control methods

Air pollution is one of main challenging issues nowadays that researchers have been trying to address.The emissions of vehicle engine exhausts are responsible for 50 percent of air pollution. Different types of emissions emit from vehicles including carbon monoxide, hydrocarbons, NOX, and so on. There is a tendency to develop strategies of engine control which work in a fast way. Accomplishing this task will result in a decrease in emissions which coupled with the fuel composition can bring about the best performance of the vehicle engine.Controlling the Air-Fuel Ratio (AFR) is necessary, because the AFR has an enormous impact on the effectiveness of the fuel and reduction of emissions.This paper is aimed at reviewing the recent studies on the prediction and control of the AFR, as a bulk of research works with different approaches, was conducted in this area.These approaches include both classical and modern methods, namely Artificial Neural Networks (ANN), Fuzzy Logic, and Neuro-Fuzzy Systems are described in this paper.The strength and the weakness of individual approaches will be discussed at length

On the design of the manifold for a race car

This paper involves the design and construction of the intake manifold system of the FSAE car including the air shroud, air filter, throttle body, restrictor plenum, fuel injectors, fuel rail and runners. To ensure the quality, the proposed system is designed based on the FSAE rules. The design process of the intake manifold system will consist of the usual engineering processes including computer modelling, Finite Element Analysis and finally Computational Fluid Dynamics testing in order to determine the validity of the model and to tune the design in order to obtain the optimum performance out of the intake manifold system as a whole

On the Design of the Manifold for a Race Car

This paper involves the design and construction of the intake manifold system of the FSAE car including the air shroud, air filter, throttle body, restrictor plenum, fuel injectors, fuel rail and runners. To ensure the quality, the proposed system is designed based on the FSAE rules. The design process of the intake manifold system will consist of the usual engineering processes including computer modelling, Finite Element Analysis and finally Computational Fluid Dynamics testing in order to determine the validity of the model and to tune the design in order to obtain the optimum performance out of the intake manifold system as a whole

PHYSICS-BASED MODELING AND CONTROL OF POWERTRAIN SYSTEMS INTEGRATED WITH LOW TEMPERATURE COMBUSTION ENGINES

Low Temperature Combustion (LTC) holds promise for high thermal efficiency and low Nitrogen Oxides (NOx) and Particulate Matter (PM) exhaust emissions. Fast and robust control of different engine variables is a major challenge for real-time model-based control of LTC. This thesis concentrates on control of powertrain systems that are integrated with a specific type of LTC engines called Homogenous Charge Compression Ignition (HCCI). In this thesis, accurate mean value and dynamic cycleto- cycle Control Oriented Models (COMs) are developed to capture the dynamics of HCCI engine operation. The COMs are experimentally validated for a wide range of HCCI steady-state and transient operating conditions. The developed COMs can predict engine variables including combustion phasing, engine load and exhaust gas temperature with low computational requirements for multi-input multi-output realtime HCCI controller design. Different types of model-based controllers are then developed and implemented on a detailed experimentally validated physical HCCI engine model. Control of engine output and tailpipe emissions are conducted using two methodologies: i) an optimal algorithm based on a novel engine performance index to minimize engine-out emissions and exhaust aftertreatment efficiency, and ii) grey-box modeling technique in combination with optimization methods to minimize engine emissions. In addition, grey-box models are experimentally validated and their prediction accuracy is compared with that from black-box only or clear-box only models. A detailed powertrain model is developed for a parallel Hybrid Electric Vehicle (HEV) integrated with an HCCI engine. The HEV model includes sub-models for different HEV components including Electric-machine (E-machine), battery, transmission system, and Longitudinal Vehicle Dynamics (LVD). The HCCI map model is obtained based on extensive experimental engine dynamometer testing. The LTC-HEV model is used to investigate the potential fuel consumption benefits archived by combining two technologies including LTC and electrification. An optimal control strategy including Model Predictive Control (MPC) is used for energy management control in the studied parallel LTC-HEV. The developed HEV model is then modified by replacing a detailed dynamic engine model and a dynamic clutch model to investigate effects of powertrain dynamics on the HEV energy consumption. The dynamics include engine fuel flow dynamics, engine air flow dynamics, engine rotational dynamics, and clutch dynamics. An enhanced MPC strategy for HEV torque split control is developed by incorporating the effects of the studied engine dynamics to save more energy compared to the commonly used map-based control strategies where the effects of powertrain dynamics are ignored. LTC is promising for reduction in fuel consumption and emission production however sophisticated multi variable engine controllers are required to realize application of LTC engines. This thesis centers on development of model-based controllers for powertrain systems with LTC engines

Modélisation et contrôle du moteur à allumage commandé pour Euro 6

Cette thèse a été développé grâce à une Conventions Industrielles de Formation par la Recherche (CIFRE). Cette convention fait partie d un programme de l Association nationale de la recherche et de la technologie (ANRT), coordonné par le Centre nationale de la recherche scientifique (CNRS). L accord CIFRE subventionne les entreprises françaises qui engagent un thésard pour conduire un projet scientifique dans l entreprise, en partenariat avec un laboratoire publique de recherche. Pour cette thèse, l accord CIFRE a été signé par Renault et les laboratoires GIPSA Lab de Grenoble et PRISME d Orléans.Cette thèse se focalise sur la modélisation 0D, en particulier sur une description plus détaillé du processus de la combustion et l estimation des masses enfermées dans la chambre de combustion d un moteur à allumage commandé (Spark Ignited (SI) engine). Les principaux développements comportent des points suivants :- L impact flamme parois pendant la combustion : un nouveau modèle pour prendre en compte ce phénomène dans le cadre d un modèle de combustion 0D à deux zones a été développé. Ce modèle permet de prendre en compte la géométrie de la chambre de combustion et la proportion de flamme que brûle proche des parois du cylindre. Plusieurs études ont montré qu une grand proportion (20% au 30%) du mélange frais brûle dans ce mode de combustion ce qui montre l importance de prendre en compte ce phénomène. - L estimation de la mass totale enfermée dans la chambre de combustion après la fermeture des soupapes est un phénomène très intéressant qui présente un Challenger pour les chercheurs motoristes. Une estimation plus précise de la mass enfermée dans la chambre de combustion permet d avoir un meilleur contrôle de l injection du carburant et une amélioration dans le traitement des polluants.- Le dernier point à traiter dans cette thèse est la commande d un système d injection common rail . Ce point a pour but de compléter la modélisation de la combustion en ajoutant une thématique liée à l injection, lequel est un paramètre crucial dans le processus de la combustion. L objectif d un système d injection common rail est de contrôler l avance de l injection, la durée et la pression, de façon indépendante dans chaque cylindre, pour avoir un meilleur contrôle de la combustion, en dépendant des conditions d opération. Cette injection permet de réguler le carburant en quantités très petites, ce qu aide à réduire la consommation, les émissions polluantes, et aussi à améliorer la performance du moteur.This thesis has been developed thanks to a Conventions Industrielles de Formation par la Recherche (CIFRE)1 agreement, that is a program of the french agency Association nationale de la recherche et de la Technologie(ANRT), coordinated by the Centre nationale de la recherche scientifique (CNRS). The CIFRE program grants the Franch companies who engage a PhD student to carry out a research project of the company within a public research lab. For this thesis, a CIFRE agreement has been accorded between the automobile company Renault France and the scientific laboratories Gipsa Lab in Grenoble and PRISME in Orléans.This thesis is focused on the modeling of a detailed description of the 0D combustion process and the estimation of the enclosed mass in the combustion chamber for a Spark Ignited (SI) engine. The main developments are summarized as follows:- The combustion process is frequently modeled as growing flame inside of the combustion chamber. Many 0D thermodynamical Engine models mostly focus on the laminar characteristics of such a free developing ame, but they lack of a suitable approximation of the combustion when the ame reaches the cylinder walls. In this thesis, a flame-wall interaction model is proposed as a complement of a 0D two zones thermodynamical model.- The estimation of the total mass enclosed in the combustion chamber is an interesting and challenging task for the engine control community. In this thesis, two nonlinear observers are synthesized for the enclosed mass estimation: a classical nonlinear high gain observer and an extended linear parameter varying (LPV) high gain observer.- A controller for a common rail injection system is developed in this thesis. First, an input state linearization of a common rail model is performed, in order to overcome the strong nonlinearities and build a virtual linear model. Using the virtual model, two linear control strategies are implemented to regulate the common rail pressure: an optimal linear quadratic regulator LQR with integral action and an optimal LQR tracking (feedforward) with integral action strategy.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF