A Soft Sensor-Based Fault-Tolerant Control on the Air Fuel Ratio of Spark-Ignition Engines

The air/fuel ratio (AFR) regulation for spark-ignition (SI) engines has been an essential and challenging control problem for engineers in the automotive industry. The feed-forward and feedback scheme has been investigated in both academic research and industrial application. The aging effect can often cause an AFR sensor fault in the feedback loop, and the AFR control performance will degrade consequently. In this research, a new control scheme on AFR with fault-tolerance is proposed by using an artificial neural network model based on fault detection and compensation, which can provide the satisfactory AFR regulation performance at the stoichiometric value for the combustion process, given a certain level of misreading of the AFR sensor

Optimal air and fuel-path control of a diesel engine

The work reported in this thesis explores innovative control structures and controller design for a heavy duty Caterpillar C6.6 diesel engine. The aim of the work is not only to demonstrate the optimisation of engine performance in terms of fuel consumption, NOx and soot emissions, but also to explore ways to reduce lengthy calibration time and its associated high costs. The test engine is equipped with high pressure exhaust gas recirculation (EGR) and a variable geometry turbocharger (VGT). Consequently, there are two principal inputs in the air-path: EGR valve position and VGT vane position. The fuel injection system is common rail, with injectors electrically actuated and includes a multi-pulse injection mode. With two-pulse injection mode, there are as many as five control variables in the fuel-path needing to be adjusted for different engine operating conditions. [Continues.

MODEL-BASED CONTROL OF AN RCCI ENGINE

Reactivity controlled compression ignition (RCCI) is a combustion strategy that offers high fuel conversion efficiency and near zero emissions of NOx and soot which can help in improving fuel economy in mobile and stationary internal combustion engine (ICE) applications and at the same time lower engine-out emissions. One of the main challenges associated with RCCI combustion is the difficulty in simultaneously controlling combustion phasing, engine load, and cyclic variability during transient engine operations. This thesis focuses on developing model based controllers for cycle-to-cycle combustion phasing and load control during transient operations. A control oriented model (COM) is developed by using mean value models to predict start of combustion (SOC) and crank angle of 50% mass fraction burn (CA50). The COM is validated using transient data from an experimental RCCI engine. The validation results show that the COM is able to capture the experimental trends in CA50 and indicated mean effective pressure (IMEP). The COM is then used to develop a linear quadratic integral (LQI) controller and model predictive controllers (MPC). Premixed ratio (PR) and start of injection (SOI) are the control variables used to control CA50, while the total fuel quantity (FQ) is the engine variable used to control load. The selection between PR and SOI is done using a sensitivity based algorithm. Experimental validation results for reference tracking using LQI and MPC show that the desired CA50 and IMEP can be attained in a single cycle during step-up and step-down transients and yield an average error of less than 1.6 crank angle degrees (CAD) in the CA50 and less than 35 kPa in the IMEP. This thesis presents the first study in the literature to design and implement LQI and MPC combustion controllers for RCCI engines

Air path and combustion controls coordination in diesel engine

The tightening of the diesel pollutants emissions regulations has made the performances obtainable from steady-state map controls, commonly employed in Internal Combustion Engine (ICE) management, unsatisfactory. To overcome these performance limitations, control systems have to cope with the engine transient operation conditions, coupling between its subsystem dynamics, and the trade-off between different requirements to efficiently manage the engine. The work demonstrates the deployment of a reference generator that coordinates the air path and combustion control systems of a turbocharged diesel engine for heavy-duty applications. The control system coordinator is based on neural networks and allows to exploit the best performance of the two control systems. The key idea is to generate air path targets, intake O2 concentration and Intake MAnifold Pressure (IMAP), coherent with the ones of the combustion control system, engine load and engine-out Nitrogen Oxides (NOx). In this way, the air path control system provides the global conditions for the correct functioning of the engine, while, in cooperation, the combustion control will react to fast changes in the engine operating state and compensate for the remaining deviations with respect to load and NOx targets. Reference generator networks are suitable for further real-time implementation on rapid-prototyping hardware and their performance was overall good

Real-time predictive control for SI engines using linear parameter-varying models

As a response to the ever more stringent emission standards, automotive engines have become more complex with more actuators. The traditional approach of using many single-input single output controllers has become more difficult to design, due to complex system interactions and constraints. Model predictive control offers an attractive solution to this problem because of its ability to handle multi-input multi-output systems with constraints on inputs and outputs. The application of model based predictive control to automotive engines is explored below and a multivariable engine torque and air-fuel ratio controller is described using a quasi-LPV model predictive control methodology. Compared with the traditional approach of using SISO controllers to control air fuel ratio and torque separately, an advantage is that the interactions between the air and fuel paths are handled explicitly. Furthermore, the quasi-LPV model-based approach is capable of capturing the model nonlinearities within a tractable linear structure, and it has the potential of handling hard actuator constraints. The control design approach was applied to a 2010 Chevy Equinox with a 2.4L gasoline engine and simulation results are presented. Since computational complexity has been the main limiting factor for fast real time applications of MPC, we present various simplifications to reduce computational requirements. A benchmark comparison of estimated computational speed is included

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

An improved rate of heat release model for modern high speed diesel engines

To meet the increasingly stringent emissions standards, diesel engines need to include more active technologies with their associated control systems. Hardware-in-the-loop (HiL) approaches are becoming popular where the engine system is represented as a real-time capable model to allow development of the controller hardware and software without the need for the real engine system. This paper focusses on the engine model required in such approaches. A number of semi-physical, zero-dimensional combustion modeling techniques are enhanced and combined into a complete model, these include—ignition delay, premixed and diffusion combustion and wall impingement. In addition, a fuel injection model was used to provide fuel injection rate from solenoid energizing signals. The model was parameterized using a small set of experimental data from an engine dynamometer test facility and validated against a complete data set covering the full engine speed and torque range. The model was shown to characterize the rate of heat release (RoHR) well over the engine speed and load range. Critically, the wall impingement model improved R2 value for maximum RoHR from 0.89 to 0.96. This was reflected in the model's ability to match both pilot and main combustion phasing, and peak heat release rates derived from measured data. The model predicted indicated mean effective pressure and maximum pressure with R2 values of 0.99 across the engine map. The worst prediction was for the angle of maximum pressure which had an R2 of 0.74. The results demonstrate the predictive ability of the model, with only a small set of empirical data for training—this is a key advantage over conventional methods. The fuel injection model yielded good results for predicted injection quantity (R2 = 0.99) and enabled the use of the RoHR model without the need for measured rate of injection.</jats:p

An improved rate of heat release model for modern high speed diesel engines

To meet the increasingly stringent emissions standards, diesel engines need to include more active technologies with their associated control systems. Hardware-in-the-loop (HiL) approaches are becoming popular where the engine system is represented as a real-time capable model to allow development of the controller hardware and software without the need for the real engine system. This paper focusses on the engine model required in such approaches. A number of semi-physical, zero-dimensional combustion modeling techniques are enhanced and combined into a complete model, these include—ignition delay, premixed and diffusion combustion and wall impingement. In addition, a fuel injection model was used to provide fuel injection rate from solenoid energizing signals. The model was parameterized using a small set of experimental data from an engine dynamometer test facility and validated against a complete data set covering the full engine speed and torque range. The model was shown to characterize the rate of heat release (RoHR) well over the engine speed and load range. Critically, the wall impingement model improved R2 value for maximum RoHR from 0.89 to 0.96. This was reflected in the model's ability to match both pilot and main combustion phasing, and peak heat release rates derived from measured data. The model predicted indicated mean effective pressure and maximum pressure with R2 values of 0.99 across the engine map. The worst prediction was for the angle of maximum pressure which had an R2 of 0.74. The results demonstrate the predictive ability of the model, with only a small set of empirical data for training—this is a key advantage over conventional methods. The fuel injection model yielded good results for predicted injection quantity (R2 = 0.99) and enabled the use of the RoHR model without the need for measured rate of injection

Modeling and control of fuel cell-battery hybrid energy sources

Environmental, political, and availability concerns regarding fossil fuels in recent decades have garnered substantial research and development in the area of alternative energy systems. Among various alternative energy systems, fuel cells and batteries have attracted significant attention both in academia and industry considering their superior performances and numerous advantages. In this dissertation, the modeling and control of these two electrochemical sources as the main constituents of fuel cell-battery hybrid energy sources are studied with ultimate goals of improving their performance, reducing their development and operational costs and consequently, easing their widespread commercialization. More specifically, Paper I provides a comprehensive background and literature review about Li-ion battery and its Battery Management System (BMS). Furthermore, the development of an experimental BMS design testbench is introduced in this paper. Paper II discusses the design of a novel observer for Li-ion battery State of Charge (SOC) estimation, as one of the most important functionalities of BMSs. Paper III addresses the control-oriented modeling and analysis of open-cathode fuel cells in order to provide a comprehensive system-level understanding of their real-time operation and to establish a basis for control design. Finally, in Paper IV a feedback controller, combined with a novel output-injection observer, is designed and implemented for open-cathode fuel cell temperature control. It is shown that temperature control not only ensures the fuel cell temperature reference is properly maintained, but, along with an uncertainty estimator, can also be used to adaptively stabilize the output voltage --Abstract, page iv