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NOx and PM are the critical emissions to meet the legislation limits for diesel engines. Often a value for these emissions is needed online for on-board diagnostics, engine control, exhaust aftertreatment control, model-based controller design or model-in-the-loop simulations. Besides the obvious method of measuring these emissions, a sensible alternative is to estimate them with virtual sensors. A lot of literature can be found presenting different modeling approaches for NOx emissions. Some are very close to the physics and the chemical reactions taking place inside the combustion chamber, others are only given by adapting general functions to measurement data. Hence, generally speaking, there is not a certain method which is seen as the solution for modeling emissions. Finding the best model approach is not straightforward and depends on the model application, the available measurement channels and the available data set for calibration.

Nitrogen oxide (NOx) sensing is required both for on-board diagnosis and optimal selective catalytic reduction (SCR)-catalyst control in heavy duty diesel engines. This can be accomplished either by physical solid-state sensors, or by so-called virtual sensors, which estimate the value of the target quantity using other states by means of a model. Both approaches have advantages and disadvantages. This paper resumes the derivation and the identification of a virtual sensor based on a polynomial structure and optimal experimental design methods and compares its performance to the one of a production physical solid state sensor. The virtual sensor is compared with a commercially available solid-state sensor in terms of accuracy (stationary as well as dynamic) and operation limits.

Complexity and nonlinearity of engines makes precise first principle engine models often difficult to obtain, as for instance for emissions. System identification is a well known possible alternative, successfully used in several automotive applications. In most cases system identification is concerned with the estimation of the unknown parameters of a known set of equations. Unfortunately, for many engine subsystems, there is no sufficiently precise or real time suitable model. This paper presents a sequential algorithm which allows to derive real time suitable models on line by a combination of model structure hypothesis of increasing complexity and an associated optimal input design and selection process. This paper introduces the method and shows its use both for a rather simple and a very difficult engine identification task, a dynamical model of the airpath of a Diesel engine and a dynamical model of nitrogen oxides and particulate matter.

Today’s racing motorcycles can provide engine torques higher than the rear tire can transfer to the road. In motorcycle applications, rear wheel torque control can be critical not only for performance, but also for safety, especially in or out of curves. While most drivers can achieve rather good results in handling this high torque on a straight road, the same is not true during cornering. In this paper we present a strategy for traction control which enforces safety while keeping high acceleration even for large roll angles. The proposed control strategy is tested on a motorcycle simulator and performance could be shown during different demonstrative maneuvers.

Emission abatement is one of the main targets in engine development and design today. Modern turbocharged CRDI Diesel engines with variable turbine geometry (VTG) and exhaust gas recirculation (EGR) provide new degrees of freedom for air path control with enormous effects on emissions. Exploiting these degrees of freedom usually involves a huge calibration work, as sensors are available only for few quantities and dynamical models are mostly not available, so feedback or model based optimization is hardly possible. This paper presents a time efficient data based strategy to obtain such models yielding an accurate as well as robust emission model for nitrogen oxides (NOx) and particulate matter (PM) by means of design of experiment. The model output is generated by smoothly switching between local models, representing different engine operating points. An adapted D-optimal design of experiments strategy provides optimal data for model identification.

Two-wheel vehicles are becoming continuously more important in Europe, but their spread is accompanied by an increase in security concerns due a number of reasons. These include stability problems during braking, and in particular curve braking, which is much more critical than in 4-wheel vehicles. These stability problems are strongly influenced by the behavior of the driver, in particular by his braking and steering activity. In this work we present a curve-safe ABS control, and analyze the role of the driver by a simulation model. It turns out that the demands on the driver in terms of stability control vary strongly with the braking behavior.