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Exhaust pressures (P3) are hard parameters to measure and can be readily estimated, the cost of the sensors and the temperature in the exhaust system makes the implementation of an exhaust pressure sensor in a vehicle control system a costly endeavor. The contention with measured P3 is the accuracy required for proper engine and vehicle control can sometimes exceed the accuracy specification of market available sensors and existing models. A turbine inlet exhaust pressure observer model based on isentropic expansion and heat transfer across a turbocharger turbine was developed and investigated in this paper. The model uses 4 main components; an open loop P3 orifice flow model, a model of isentropic expansion across the turbine, a turbine and pipe heat transfer models and an integrator with the deviation in the downstream turbine outlet parameter.

Designing a control system that can robustly detect faulted emission control devices under all environmental and driving conditions is a challenging task for OEMs. In order to gain confidence in the control strategy and the values of tunable parameters, the test vehicles need to be subjected to their limits during the development process. Complexity of modern powertrain systems along with the On-Board Diagnostic (OBD) monitors with multidimensional thresholds make it difficult to anticipate all the possible scenarios. Finding optimal solutions to these problems using traditional calibration processes can be time and resource intensive. A possible solution is to take a data driven calibration approach. In this method, a large amount of data is collected by collaboration of different groups working on the same powertrain. Later, the data is mined to find the optimum values of tunable parameters for the respective vehicle functions.

The resistive particulate matter sensor (PMS) is rapidly becoming ubiquitous on diesel vehicles as a means to diagnose particulate filter (DPF) leaks. By design the device provides an integrated measure of the amount of PM to which it has been exposed during a defined measurement period within a drive cycle. The state of the art resistive PMS has a large deadband before any valid output related to the accumulated PM is realized. As a result, most DPF monitors that use the PMS consider its output only as an indicator that a threshold quantity of PM has amassed rather than a real-time measure of concentration. This measurement paradigm has the unfortunate side effect that as the PM OBD threshold decreases, or the PMS is used on a vehicle with a larger exhaust volume flow, a longer measurement is required to reach the same PM sensor output. Longer PMS measurement times lead to long particulate filter monitoring durations that may reduce filter monitor completion frequency.

The pre EGR valve pressure is an important measurement for the Diesel engine air handling system. It is commonly used for the EGR flow calculation during engine transient operation. Due to the erosive exhaust gas, an EGR pressure sensor will eventually have gold corrosion resulting in drive-ability issues. Therefore, a software replacement for the EGR pressure sensor is desirable. However, when the EGR valve is on the cold side of the EGR cooler, the accuracy of the EGR pressure estimation deteriorates because of the variability of the pressure drop across the EGR cooler due to EGR cooler fouling. In this paper, an adaptive scheme is developed to improve the accuracy of pre EGR valve pressure estimation in the presence of EGR cooler fouling for diesel engines. The pressure drop across the EGR cooler is shown to be proportional to the velocity pressure of the EGR flow through the cooler.

Lean NOx traps are considered as a possible means to reduce diesel powertrain tail pipe NOx emissions to future stringent limits. Several publications have proposed models for lean NOx traps [1, 2, 3 and 4]. This paper focuses on a lean NOx trap model that can be used for the verification of control strategies before these strategies are implemented in target microprocessors. Strategy verification in a simulation environment is a crucial tool for reducing control strategy development and implementation time.

Diesel particulate filters (DPFs) are a technology likely to be deployed to meet future stringent emission levels for heavy and light duty diesel powertrains in North America and Europe. This paper discusses experimental results in the active regeneration of DPFs. Attention is given to the system components, the information based on which regeneration is triggered, and the means to achieve a regeneration. The paper will report on successful regenerations under several extreme conditions.

This paper presents some of the challenges involved in diagnosing leaks in diesel particulate filters (DPFs). It concentrates on diagnosis with a pressure sensor. It is argued that not all failure modes can be detected by such a sensor, and that this method of diagnosis has far-reaching implications on the monitor completion frequency. Via an error analysis of commercially available sensors we argue that there is little to no separation between healthy and damaged particulate filters. The challenges are illustrated with straightforward analytical calculations.

An electromechanically driven valve train offers unprecedented flexibility to optimize engine operation for each speed load point individually. One of the main benefits is the increased fuel economy resulting from unthrottled operation. The absence of a restriction at the entrance of the intake manifold leads to wave propagation in the intake system and makes a direct measurement of air flow with a hot wire air meter unreliable. To deliver the right amount of fuel for a desired air-fuel ratio, we therefore need an open loop estimate of the air flow based on measureable or commanded signals or quantities. This paper investigates various expressions for air charge in camless engines based on quasi-static assumptions for heat transfer and pressure.

A control strategy is presented to limit the rate of heat release by Diesel Particulate Filters (DPF) during regeneration reactions between oxygen and the collected soot. Heat release is managed by limiting the oxygen supplied to the DPF, which limits the rate of the regeneration reaction. Three actuators are used to control the amount of oxygen flowing in the exhaust system: an exhaust gas re-circulation (EGR) valve, an intake throttle (ITH), and a hydrocarbon injector located upstream of the DPF in the exhaust system. The EGR valve and ITH are low-bandwidth actuators that control slowly varying changes in oxygen flow, while the hydrocarbon injector is a high-bandwidth actuator that controls the corresponding fast changes in oxygen flow.

This paper investigates model free approaches to monitor the Exhaust Gas Recirculation (EGR) for a diesel engine equipped with EGR cooler and EGR cooler bypass valve. A conventional way of monitoring the EGR cooler is a model based approach which involves modeling the EGR cooler effectiveness and compares the modeled (estimated) EGR cooler effectiveness (or EGR cooler downstream temperature) and the measured EGR cooler effectiveness (or EGR cooler downstream temperature). The model based approach has the advantage of being portable across many different cooler configurations, but it requires modeling/calibration efforts and necessary temperature measurements. The EGR cooler downstream temperature serves several roles. It can be used together with the fresh air temperature to calculate the charge air temperature. It also can be utilized to monitor the performance of the EGR cooler as mentioned above.

While not commonly in production today, Gasoline Particulate Filters (GPFs) are likely to see widespread deployment to meet stringent EU6.2 and China particulate number (PN) standards. In many ways the operating conditions for GPFs are orthogonal to those of their diesel counterparts, and this leads to different and interesting requirements for the control strategy. We will present some generic system architectures for exhaust systems containing a GPF and will lay out an architecture for the GPF control strategy components which include: regeneration assist feature, soot estimation algorithm, GPF protection. The regeneration assist feature uses spark retard to increase exhaust temperature. The soot estimation algorithm describes how we can estimate soot from an open loop model or from a normalized pressure metric. The GPF protection feature controls oxygen flow to limit the soot burn rate. We will show validation data of the control strategy under different operating conditions.

Over the past decade urea-based selective catalytic reduction (SCR) has become a leading aftertreatment solution to meet increasingly stringent Nitrogen oxide (NOx) emissions requirements in diesel powertrains. A common trend seen in modern SCR systems is the use of "split-brick" configurations where two SCR catalysts are placed in thermally distinct regions of the aftertreatment. One catalyst is close-coupled to the engine for fast light-off and another catalyst is positioned under-floor to improve performance at high space velocities. Typically, a single injector is located upstream of the first catalyst to provide the reductant necessary for efficient NOx reduction. This paper explores the potential benefit, in terms of improved NOx reduction, control of NH3 slip or reduced reductant consumption, of having independently actuated injectors in front of each catalyst.

Many excellent papers have been written about the subject of estimating engine-out NOx on diesel engines based on real-time available data. The claimed accuracy of these models is typically around 6-10% on validation data sets with known inputs. This reported accuracy typically ignores input uncertainties, thus arriving at an optimistic estimate of the model accuracy in a real-time application. In our paper we analyze the effect of input uncertainty on the accuracy of engine-out NOx estimates via a numerical Monte Carlo simulation and show that this effect can be significant. Even though our model is based on an in-cylinder pressure sensor, this sensor is limited in its capability to reduce the effect of other measured inputs on the model.

To meet stringent 2010 NOx emissions, many manufacturers are expected to deploy urea selective catalytic reduction systems. Indications from ARB are that a threshold monitor must be developed to monitor their performance. The most capable monitoring technology at this time relies on NOx sensors. This paper assesses the capability of the NOx sensor as an SCR monitoring device. To this end, the NOx sensor must be able to distinguish between a marginal and a threshold catalyst with enough separation to allow for variability. We present the noise factors associated with the NOx conversion of the SCR system, and analyze what NOx sensor accuracy we need to preserve separation in the face of those noise factors. It is shown that a 1.75 threshold monitor is not feasible with current NOx sensor technology. We analyze the benefit of a partial volume monitor, and show there is no advantage unless the slope error of the NOx sensor is drastically reduced from current levels.

Modern diesel engines are frequently equipped with an exhaust gas recirculation (EGR) valve and intake throttle (ITH). The intake throttle serves to depress intake manifold pressure, create a greater pressure differential across the EGR valve, and enhance the ability to flow EGR at low speed and load conditions. A conventional approach to controlling the intake throttle is to schedule its desired position as a function of engine speed and load. In this paper we present a coordinated approach to the control of EGR valve and intake throttle.

Regulatory authorities are actively revising and updating the rules for on board diagnostics of diesel powertrains. Diesel oxidation catalysts are among the parts that will have to be monitored. This paper discusses some of the issues related to the feasibility of monitoring these catalysts. We concentrate on the effect of real world noise factors on the ability to distinguish marginal from threshold catalysts and demonstrate that with current sensor and catalyst technology the separation between the two is poor.

This paper presents heat release regressions for the combustion modes in a gasoline direct injected (GDI) engines. These heat release regressions can be used in one-dimensional flow codes and mean value models as a simplified representation of combustion. The heat release profiles are approximated as functions of crank angle, with some free parameters. These parameters are fitted as functions of engine operating conditions, so that a continuous family of curves is obtained. GDI engines have four distinct modes of combustion: homogeneous rich, homogeneous stoichiometric, homogeneous lean and stratified lean, each requiring its own functional approximation. We present the functional approximations, the dependence of the parameters on engine operating conditions, and the quality of the fit on data from a 1.8 liter production GDI engine.

This paper describes and experimentally compares various strategies to control the gas flows of a high speed automotive diesel engine equipped with external exhaust gas recirculation (EGR) valve and variable geometry turbocharger (VGT). The strategies differ in their choice of controlled variables and design approaches. Some of the strategies rely on standard measurements such as intake manifold pressure and compressor mass air flow. Others measure or estimate less readily available quantities such as exhaust manifold pressure and EGR flow fraction. The focus of this paper is on the experimental details of controller implementation. The control schemes are evaluated on their emissions and fuel economy over the extra urban part of the new European drive cycle. The implementation effort and chronometric load are also compared.

Gasoline particulate filters (GPFs) are extremely effective at reducing tailpipe emissions of particulate mass and particulate number. Especially in the European and Chinese markets, where a particulate number standard is legislated, we see gasoline particulate filters being deployed in production on gasoline direct injected engines. Due to the high temperature in gasoline exhaust, most applications are expected to be passively regenerating without the help of an active regeneration strategy. However, for the few cases where a customer drive cycle has consistently low speed over a long time frame, an active regeneration strategy may be required. This involves increasing the exhaust temperature at the GPF up to around 600 degC so that soot can be combusted. We compare two different ways of achieving these temperatures, namely spark retard and air fuel ratio modulation. The former generates heat in the engine, the latter generates heat in one or more catalysts in the exhaust system.

This paper presents the application of model predictive control (MPC) to DOC temperature control during DPF regeneration. The model predictive control approach is selected for its advantage - using a model to optimize control moves over horizon while handling constraints. Due to the slow thermal dynamics of the DOC and DPF, computational bandwidth is not an issue, allowing for more complex calculations in each control loop. The control problem is formulated such that all the engine control actions, other than far post injection, are performed by the existing production engine controller, whereas far post injection is selected as the MPC manipulated variable and DOC outlet temperature as the controlled variable. The Honeywell OnRAMP Design Suite (model predictive control software) is used for model identification, control design and calibration.