Search

This paper aims at developing integrated onboard diagnosis and fault-tolerant control methods with experimental validation for a urea selective catalyst reduction (SCR) aftertreatment system to reduce vehicle tailpipe emissions. Diagnostics are performed for an SCR urea injection system by estimating and monitoring the injected urea mass flow with no need for a costly physical flow sensor. The estimation is derived from a first-principle-based urea injection system model, and the model parameters are identified by using system identification. During vehicle transient maneuvers, a Kalman filter (KF) is formulated to further reduce the estimation noise and improve diagnostic robustness. Once an injection fault is detected, an adaptation control algorithm is applied to compensate the urea injection command, thus correcting certain types of urea under/overdosing faults and maintaining the SCR $\mbox{NO}_{x}$ conversion performance. These methods have been validated through vehicle tests by utilizing an onboard rapid prototyping control system.

Homogeneous charge compression ignition (HCCI) is one of the most promising piston-engine concepts for the future, providing significantly improved efficiency and emissions characteristics relative to current technologies. This paper presents a framework for controlling an HCCI engine with exhaust recompression and direct injection of fuel into the cylinder. A physical model is used to describe the HCCI process, with the model states being closely linked to the thermodynamic state of the cylinder constituents. Separability between the effects of the control inputs on the desired outputs provides an opportunity to develop a simple linear control scheme, where the fuel is used to control the work output and the valve timings are used to control the phasing of combustion. The controller is tested on both a single and multi-cylinder HCCI engine, demonstrating the value of a physical model-based control approach that allows an easy porting of the control structure from one engine to another. Experimental results show good tracking of both the work output and combustion phasing over a wide operating region on both engines. In addition, the controller is able to balance out differences between cylinders on the multi-cylinder engine testbed, and reduce the cycle-to-cycle variability of combustion.

This paper discusses the application of linear observer theory to engine control with a specific focus on observers based on exhaust measurements. The motivation for this architecture is that a direct measurement of the relevant quantity (the exhaust chemistry entering the catalyst) will typically lead to the most accurate control. A secondary motivation is the need to reduce costs. If sufficient accuracy can be obtained with the exhaust sensor providing the primary information, the cost associated with intake manifold sensors can be reduced. The basic theory is discussed and experimental results are reported that illustrate the benefits and superior performance of linear observer based control.

Increasingly strict emission standards require very accurate and fast air-fuel (A/F) ratio control. Theory, implementation, and experiments for a model-based A/F ratio control system using state-space control and estimation methods are presented. An excellent match of the in-cylinder A/F ratio with the desired A/F ratio can be achieved in spite of the time delay in the system. This is accomplished by compensating the air dynamics with a drive-by-wire throttle supported feedforward control, and the fuel dynamics with an observer-based feedback control. For fast correction of steady-state system errors, the control law is extended by learning control. The proposed method is applicable to multicylinder production engines. Experimental validation is made on a single-cylinder research engine, with A/F ratio feedback from either a linear or a nonlinear exhaust gas oxygen sensor. >