Search Results

This paper analyzes the use of an ammonia sensor for feedback control in diesel exhaust systems. We build our case around the specific example of the heavy duty transient cycle, and an exhaust system with an SCR catalyst, a single urea injector and an upstream and downstream NOx sensor. A key component in our analysis is the inclusion of the tolerance of the ammonia sensor. We show that with the current understanding of the sensor tolerance, the ammonia sensor has limited benefit for controls.

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.

The availability of connectivity and autonomy enabled resources, within the automotive sector, has primarily been considered for driver assist technologies and for extending the levels of vehicle autonomy. It is not a stretch to imagine that the additional information, available from connectivity and autonomy, may also be useful in further improving powertrain functions. Critical powertrain subsystems that must operate with limited or uncertain knowledge of their environment stand to benefit from such new information sources. Unfortunately, the adoption of this new information resource has been slow within the powertrain community and has typically been limited to the obvious problem choices such as battery charge management for electric vehicles and efforts related to fuel economy benefits from adaptive/coordinated cruise control. In this paper we discuss the application of connectivity resources in the management of an aftertreatment sub-system, the Diesel Particulate Filter (DPF).

Gasoline particulate filters (GPF) are used as an efficient solution to reduce particulate matter (PM) emissions on gasoline vehicles. GPFs are ceramic wall-flow filters and are normally located downstream of conventional three-way catalysts (TWC) [1]. The study in this paper is intended to evaluate the impact of drive cycle and ambient temperature on modelled GPF soot accumulation and regeneration. The test data were obtained through real road testing in Chinese cities including Nanjing, Hainan and Harbin. Five 2.0 L gasoline turbo direct-injection (GTDI) prototype vehicles from several China Stage 6 applications were employed for the road tests. The results of the testing indicated that a drive cycle with low engine speed and engine load, like a typical city road in rush hour traffic in Nanjing, had a low probability of generating high GPF temperatures (> 600 °C) and sufficient oxygen to regenerate the GPF.

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.

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.

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.

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.

Exhaust temperatures are some of the hardest parameters to measure and estimate based on the range of the signal and the environment that an engine exhaust system creates. Accurate exhaust temperature inputs in vehicle and engine control systems are important for performance, fuel economy, emissions and aftertreatment control. A turbine inlet exhaust temperature observer model based on isentropic expansion and heat transfer across a turbocharger turbine was developed and investigated in this paper. There are 4 main components used to model the exhaust temperature; an open loop exhaust manifold gas temperature mass/energy model, an isentropic expansion across the turbine, a turbine heat transfer model and an observer using the downstream turbine outlet temperature. Another method using only a reverse isentropic expansion model and heat transfer parts of the observer model was analyzed and compared to the observer model.

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.

This paper analyzes the potential benefit of a model based DPF leakage monitor over a conventional DPF leakage monitor that checks pressure drop after a complete regeneration. We analyze the most important noise factors involved in both approaches and demonstrate that the model based leakage monitor does not improve on the conventional leakage monitor in accuracy. It does improve on completion frequency, but at the expense of a great modeling effort.

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.

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.

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.

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.

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.

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.