Stephen Yurkovich

Control-Oriented Modeling of an Air-Breathing Hypersonic Vehicle

Full simulation models for flexible air-breathing hypersonic vehicles include intricate couplings between the engine and flight dynamics, along with complex interplay between flexible and rigid modes, resulting in intractable systems for nonlinear control design. In this paper, starting from a high-fidelity model, a control-oriented model in closed form is obtained by replacing complex force and moment functions with curve-fitted approximations, neglecting certain weak couplings, and neglecting slower portions of the system dynamics. The process itself allows an understanding of the system-theoretic properties of the model, and enables the applicability of model-based nonlinear control techniques. Although the focus of this paper is on the development of the control-oriented model, an example of control design based on approximate feedback linearization is provided. Simulation results demonstrate that this technique achieves excellent tracking performance, even in the presence of moderate parameter variations. The fidelity of the truth model is then increased by including additional flexible effects, which render the original control design ineffective. A more elaborate model with an additional actuator is then employed to enhance the control authority of the vehicle, required to compensate for the new flexible effects, and a new design is provided.

Model-based calibration for battery characterization in HEV applications

In hybrid electric vehicle (HEV) applications, unlike electric vehicles, operation with the battery system requires control in a relatively limited range of state-of-charge (SoC), where best efficiency, gradual aging, and no self-damaging operations are expected. In this context, one of the main, critical technical challenges is the estimation of the SoC under vehicle operations, which typically do not involve full charging or discharging. This task is particularly arduous to accomplish in real-time, due to the complex and nonlinear behavior of the battery, as well as the inevitable presence of on-board measurement errors. In this work, we describe a model-based calibration process for capturing the important characteristics of modern batteries used in typical HEV applications. This process consists of reproducible procedural steps, including pre-specified data collection, while ultimately admitting a calibration. The resulting models are useful in HEV system control design for algorithms centered on maintaining the battery SoC, in algorithms for prognostics and diagnostics, and in prediction and estimation tasks.

Hybrid large scale system model for a DC microgrid

A microgrid power system with multiple energy sources and loads is considered in this paper. Such microgrids are common due to the needs of distributed generation, renewable energy, and hybrid power sources. The system under study consists of a large number of power converters operating over a wide range of voltages and currents, interconnected via a distribution network. Stability analysis and supervisory control design requires a good model of the system that considers different operations within the microgrid, such as voltage/current levels, bidirectional power flows, and on/off switching of the power converters. In this paper, a state variable modeling approach is presented to develop a hybrid large-scale system model of the microgrid. State variable models of individual converters linearized at different operating points are the building blocks of the model. A large-scale interconnected system model is developed for each feasible interconnection of the linearized models of the converters. The switching model, which is a combination of state based and input based switching events between these large- scale system models, is developed using hybrid system theory. The modeling approach is applied to two example systems consisting of DC-DC converters and a DC bus. The hybrid large scale system models are compared with circuit simulations to show the validity of the modeling process.

An optimal regulation strategy with disturbance rejection for energy management of hybrid electric vehicles

The energy management problem of finding the optimal split between the different sources of energy in a charge-sustaining parallel HEV, ensuring stability and optimality with respect to a performance objective (fuel consumption minimization over a driving cycle), is addressed in this paper. The paper develops a generic stability and optimality framework within which the energy management problem is cast in the form of a nonlinear optimal regulation (with disturbance rejection) problem and a control Lyapunov function is used to design the control law. Two theorems ensuring optimality and asymptotic stability of the energy management strategy are proposed and proved. The sufficient conditions for optimality and stability are used to derive an analytical expression for the control law as a function of the battery state of charge/state of energy and system parameters. The control law is implemented in a simplified backward vehicle simulator and its performance is evaluated against the global optimal solution obtained from dynamic programming. The strategy performs within 4% of the benchmark solution while guaranteeing optimality and stability for any driving cycle.

An optimal regulation strategy for energy management of hybrid electric vehicles

The issue of designing an analytical optimal solution to the problem of energy management for charge-sustaining hybrid electric vehicles is addressed. In particular, it is shown that, by suitably casting the energy management problem into a nonlinear optimal regulation problem and using an appropriate control Lyapunov function candidate, it can be proved that the state-feedback based optimal control law (with respect to minimum fuel consumption) produces a charge-sustaining behavior. We provide sufficient conditions for state feedback based control law to guarantee asymptotic stability and optimality with respect to an infinite horizon performance functional. The optimal control law is implemented in a series hybrid electric vehicle and the performance of the proposed energy management strategy is shown in simulation for a specific driving case.

A Model-based estimator of engine cylinder pressure imbalance for combustion feedback control applications

One of the principal issues of low-temperature combustion modes is caused by the imbalances in the distribution of air and EGR across the cylinders, which affects the combustion process. Cylinder to cylinder variations lead to imbalances in the cylinder pressure, indicated torque, exhaust gas thermodynamic conditions and emissions. In principle, a cylinder-by-cylinder control approach could compensate for air, residuals and charge temperature imbalance. However, in order to fully benefit from closed-loop combustion control, a feedback from each engine cylinder would be necessary to reconstruct the pressure trace. Therefore, cylinder imbalance is an issue that can be detected only in a laboratory environment, wherein each engine cylinder is instrumented with a dedicated pressure transducer. This paper describes the framework and preliminary results of a model-based estimation approach to predict the individual pressure traces in a multi-cylinder engine from the output of a crankshaft speed sensor. The objective of the estimator is to reconstruct the complete pressure trace during an engine cycle with sufficient accuracy to allow for detection of cylinder to cylinder imbalances. Starting from a model of the engine crankshaft dynamics, a sliding mode observer is designed to estimate the cylinder pressure from the crankshaft speed fluctuation measurement. The results obtained by the estimator are compared with experimental data obtained on a four-cylinder Diesel engine.

Comparative analysis of closed loop AFR control during cold start

This paper examines the air-fuel ratio (AFR) control of the spark-ignited, internal combustion engine during cold start. It has been shown that a significant amount of emissions occur during cold start and idle before the AFR closed-loop control system goes into effect. Use of combustion pressure feedback, as measured from within the engine cylinder, is investigated for AFR control during cold start in an attempt to achieve better emissions. Specifically, the combustion pressure is used to calculate the equivalent heat release duration, which is then used for feedback control. Linear and nonlinear control techniques are applied to a Ford V8 engine in a laboratory setting, and the results are compared.

Direct Fuzzy Control of Idle Speed in an Internal Combustion Engine

Abstract Idle speed control is currently an important problem in production vehicles. Increasing government regulations for vehicle efficiency motivates improvements to current systems. This paper explores the idle speed control problem with a goal of lowering idle speed, rejecting torque disturbances and reducing steady state variations in engine speed at idle. The distinguishing factor in this experimental study is that modeling and control design are carried out in the crank angle domain (as opposed to the time domain). Control schemes are designed and implemented on a Ford V8 engine. The approaches compared are РID, direct fuzzy control and fuzzy model reference learning control (FMRLC). The variable being controlled is the mass air flow through the idle bypass valve. The effect of reducing sampling rate is investigated for the PID and the direct fuzzy designs.

Air-to-Fuel Ratio Switching Frequency Control for Gasoline Engines

Modern gasoline internal combustion engines use a variety of technologies to enhance the efficiency of fresh air induction. These technologies, which include variable valve timing and variable intake geometry systems, also make it more difficult to predict the mass of fresh air that is trapped during the induction stroke of the engine because they not only affect the residual gas fraction of the trapped air charge, but also the wave dynamics of the system. As the number of controllable actuators increases, this estimation problem becomes even more difficult. As these technologies continue to develop, the importance of robustness in air-to-fuel ratio control continues to grow. This paper presents an air-to-fuel ratio control algorithm based on a switching frequency regulator that has favorable robust stability properties in the presence of both input and model errors. Instead of modeling the air path system with a simplified model, this control architecture considers the air estimate as a control input. As a result, air estimation errors behave like input errors, not modeling errors. By using the rich-to-lean and lean-to-rich air-to-fuel ratio switching frequencies of the pre-catalyst exhaust gas oxygen sensor as the primary feedback signal, the control laws are completely independent of the parameters of the plant model. The performance of this controller is demonstrated both with a robust stability analysis and through a vehicle-based experimental validation.

In-cylinder oxygen concentration estimation for diesel engines via transport delay modeling

In addition to the main injection, current diesel engines often use one or more pilot injections and one or more post injections to better control the combustion process. The mass of fuel delivered and the timing of these injections has a strong affect on the combustion temperature, the heat release rate, the torque production and the formation of harmful emissions. As the cylinder conditions change and in particular as the in-cylinder oxygen concentration changes, the fuel injection masses and timings must be adjusted to achieve a desired trade-off between emissions production and fuel consumption. Alternative combustion modes are particularly sensitive to the cylinder conditions. Incorrectly estimating the cylinder contents can cause inefficient combustion and can increase the emissions produced during transient operations. Current in-cylinder oxygen concentration estimators do not account for the transport delay of the recirculated exhaust gas and are therefore less accurate during transients. By incorporating the effects of the time-varying transport delay, the plug flow based oxygen concentration model presented in this paper is able to dynamically predict the in-cylinder oxygen concentration of every induction event. The robust performance of the proposed model is demonstrated through comparisons to a high-fidelity GT-Power engine model.