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The use of Hybrid Predictive Control for model-based propulsion control applications is considered. A Variable Displacement Engine (VDE) control problem is considered, involving both continuous-time dynamics and discrete control actions in the form of activating/deactivating the engine cylinders. Hybrid Model Predictive Control is one of the most successful hybrid control schemes and builds upon predictive control methods developed for engine torque management. The ways in which preview information can be used to improve controller performance are considered, as well as simplifications to the hybrid control algorithms to reduce the computational burden. Several hybrid control design approaches are compared using a simulation of a VDE engine. The aim is to optimize the total system behaviour to provide good torque tracking, reduced fuel consumption and smooth cylinder switching. The main contribution is the demonstration that hybrid predictive control can provide a practical solution to an engine control application with the potential to enhance performance and with options to reduce complexity.

With advances in sensing, computing and communication technologies, Connected and Automated Vehicles (CAVs) are becoming feasible. The advent of CAVs presents new opportunities to improve the energy efficiency of individual vehicles. However, testing and verifying energy-efficient autonomous driving systems are difficult due to safety considerations and repeatability. In this paper, we present a co-simulation platform to develop and test novel vehicle eco-autonomous driving technologies named InfoRich, which incorporates the information from on-board sensors, V2X communications, and map database. The co-simulation platform includes eco-autonomous driving software, vehicle dynamics and powertrain (VD&PT) model, and a traffic environment simulator. Also, we utilize synthetic drive cycles derived from real-world driving data to test the strategies under realistic driving scenarios. To build road networks from the real-world driving data, we develop an Automated Parser and Calculator for Map/Scenario named AutoPASCAL. Overall, the simulation platform provides a realistic vehicle model, powertrain model, sensor model, traffic model, and road-network model to enable the evaluation of the energy efficiency of eco-autonomous driving.

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.

A control-oriented reaction-based combustion model is implemented and used to simulate the combustion process in a diesel engine. The model integrates a homogeneous thermodynamic system with a two-step chemical reaction mechanism that consists of six species. The accuracy of the model is evaluated by comparing with experimental data from a GM 6.6L, 8 cylinder Duramax engine. The model is calibrated for different key points over the entire engine map as well as various injection timings and exhaust gas recirculation (EGR) ratio using an automated calibration algorithm. The reaction-based model is shown to provide accurate predictions of incylinder pressure, temperature, mass-fraction-burned and heat release rate. As an alternative to Wiebe-based method, this approach could lead to a better model with less calibration effort. The improvement is due to the fact that the burn rate is online calculated based upon the dominated fuel chemical components and combustion chamber properties, such as temperature, oxygen and burned gas concentration, etc.

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. >

The oxygen concentration in the intake manifold of a lean-burn engine is a key variable that is directly related to the quality of combustion. In this paper, an adaptive observer is developed to estimate the intake oxygen concentration in the intake manifold of a lean-burn engine using measurements from existing sensors with minimum computational load. Performance of the observer is evaluated using transient experimental data obtained from a diesel engine.

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 a multi-cylinder 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. Experimental results show good tracking of both the work output and combustion phasing over a wide operating region. In addition, the controller is able to balance out differences between cylinders, and reduce the cycle-to-cycle variability of combustion.Copyright © 2009 by ASME