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The Exhaust Gas Recirculation (EGR) rate is a critical parameter of turbocharged diesel engines because it determines the trade-off between NOx and particulate matter (PM) emissions. On some heavy duty engines the EGR mass flow is directly measured with a Venturibased sensor and a closed loop control system maintains EGR flow. However, on most light duty diesel engines the EGR mass flow must be estimated. This paper compares two methods for estimating EGR mass flow. The first method, referred to as the Speed Density method, serves as a baseline for comparison and uses sensors for engine speed, intake manifold pressure and temperature, as well as fresh air flow (MAF). The new, second method adds turbo speed to this sensor set, and includes additional engine modelling equations, such as the EGR valve equation and the turbine equation. Special measures are taken to allow the additional equations to execute without issue on production ECMs (Electronics Controls Modules).

The interest for NOx estimators (also known as virtual sensors or inferential sensors) has increased over the recent years due to benefits attributed to cost and performance. NOx estimators are typically installed to improve On-Board Diagnostics (OBD) monitors or to lower bill of material costs by replacing physical NOx sensors. This paper presents initial development results of a virtual engine-out NOx estimator planned for the implementation on an ECM. The presented estimator consists of an airpath observer and a NOx combustion model. The role of the airpath observer is to provide input values for the NOx combustion model such as the states of the gas at the intake and exhaust manifolds. It contains a nonlinear mean-value model of the airpath suitably transformed for an efficient and robust implementation on an ECM. The airpath model uses available sensory information in the vehicle to correct predictions of the gas states.

On downsized turbocharged engines, turbo speed is correlated with maximum engine airflow and therefore with maximum engine power. To ensure safe operation in the field, auto makers introduce significant engineering margins to the turbocharger maximum speed limit. Physical turbo speed sensors provide one way to reduce this engineering margin, but are not appropriate for some applications. An accurate mathematical estimation of turbocharger speed using virtual sensor can help reduce these margins, therefore increasing available power. This paper examines the best turbo speed estimation accuracy that can be achieved using a given set of production engine sensors. “Best” is defined in a minimax sense as the smallest turbo speed error interval achievable assuming the worst case combination of sensor and actuator errors and plant parameter mismatch.

Turbocharger maps measured on a gas stand test bench are commonly used to represent turbine and compressor performance. The maps are useful source of information for mean value modeling, engine calibration optimization, virtual sensing and feedback control design. For some tasks, representing the maps by fitted functional forms can be more convenient than using interpolation of the map data directly. The functional representation usually allows for wider extrapolation ranges and more reliable application of numerical optimization methods. In literature most successful functional forms chosen to represent the compressor flow characteristics are based on rational polynomials of dimensionless head and flow parameters. Turbine flow characteristics, on the other hand, are commonly modeled as orifices or orifices with variable cross-section in case of variable geometry turbines (VGT).

Numerous studies describe the fuel consumption benefits of changing the powertrain temperature based on vehicle operating conditions. Actuators such as electric water pumps and active thermostats now provide more flexibility to change powertrain operating temperature than traditional mechanical-only systems did. Various control strategies have been proposed for powertrain temperature set-point regulation. A characteristic of powertrain thermal management systems is that the operating conditions (speed, load etc) change continuously to meet the driver demand and in most cases, the optimal conditions lie on the edge of the constraint envelope. Control strategies for set-point regulation which rely purely on feedback for disturbance rejection, without knowledge of future disturbances, might not provide the full fuel consumption benefits due to the slow thermal inertia of the system.

The paper examines how the issue of lengthy development times can be mitigated by adopting a multivariable physics based control method for the development and deployment of complex engine control algorithms required for modern diesel engines equipped with Lean NOx Trap aftertreatment technology. The proposed approach facilitates manufacturers to consider lower cost powertrain configurations for selected markets while maintaining higher performance configurations for other markets. The contribution includes on-engine results from joint work between General Motors and Honeywell. The Honeywell OnRAMP Design Suite which applies model predictive control techniques was used for model identification, control design (using model predictive control) and its calibration. With no prior work on the engine this process of calibrating an engine model and achieving transient drive cycle control on the engine required ten days in the test cell and five days of offline work using the OnRAMP software.