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In practical applications of ammonia SCR aftertreatment systems using urea as the reductant storage compound, one major difficulty is the often constrained packaging envelope. As a consequence, complete mixing of the urea solution into the exhaust gas stream as well as uniform flow and reductant distribution profiles across the catalyst inlet face are difficult to achieve. This paper discusses a modeling approach, where a combination of 3D CFD and a lumped parameter SCR model enables the prediction of system performance, even with non-uniform exhaust flow and ammonia distribution profiles. From the urea injection nozzle to SCR catalyst exit, each step in the modeling process is described and validated individually. Finally the modeling approach was applied to a design study where the performance of a range of urea-exhaust gas mixing sections was evaluated.

The availability of cylinder pressure sensors for use in production engines opens new possibilities for engine control. The use of these sensors allows reliable cylinder-individual measurement and control of the combustion process. To gain improvements in fuel economy and power output through the use of this information, appropriate models have to be developed. This paper describes a model for the estimation of the air mass as well as the residual gas fraction within the cylinder of a spark ignition (SI) engine. It has been found that the model allows good estimates within the normal operating range of an SI engine running at stoichiometric conditions.

The wall-wetting dynamics represent a very important subsystem of the air/fuel path of an SI engine. The precise feedforward control of the air/fuel ratio requires a valid model of the wall-wetting dynamics over the whole operating region of the engine. A global wall-wetting model has been developed for a production SPFI gasoline engine. This model is capable of describing the wall-wetting dynamics not only in a fixed operating point, but also for radical changes of the operating point. Its structure specifically allows for model-based compensator design and on-line parameter identification. Earlier, related publications discussed linear model structures. Those models described the dynamics around a fixed operating point only. This paper shows how one global model for the whole operating range can be constructed from a linear model and its parameter range.

In modern spark-ignited engines the accurate estimation of the amount of fuel to be injected is an important issue, in particular if a specific air-to-fuel ratio is required. The knowledge of the events occurring between the intake duct (injectors) and the exhaust duct (λ-sensor) is thus very important. Among all the systems that play a role, the best studied are the wall-wetting dynamics. Nowadays, the wall-wetting effects are compensated on the basis of simple linear models that are tuned with the help of a large number of measurements. These models are quite effective but they cannot be used universally.Their extrapolation for a non-measured operating point can lead to unsatisfactory results. Other problems arise at operating points where direct measurements are difficult, e.g., at cold start. Complex models already exist, but usually they require a lot of work in the parameterization phase.

The need for a stoichiometric air-to-fuel ratio in an SI engine with a catalytic converter makes the accurate knowledge of the air and fuel paths indispensable. This investigation is focused on the prediction of the air mass flow into the cylinder without the use of an air mass flow meter. A dynamical mean value engine model of the intake manifold has been derived. Combining a gain-scheduling and a self-tuning algorithm has been found to be a good strategy for the persistent adaptation of the intake manifold model to the changing ambient conditions and actuator parameters such as aging or malfunctions. The adaptation algorithm is based on the direct identification of the air mass flows entering and leaving the intake manifold, thus the identified parameters can be interpreted as the throttle and the filling characteristics. The recursive least squares algorithm has been used for parameter identification.

Instantaneous in-cylinder pressure, a key variable in the improvement of engine performance and reduction of emissions, is not likely to be measured directly in production type engines in the near future. As a countermeasure, a pressure estimation method based on physical first principles for the estimation of the instantaneous in-cylinder pressure of an SI engine using measured crankshaft angular velocity is presented here. The approach consists of (a) mapping the model parameters at nominal operating conditions and (b) adapting the model parameters to current operating conditions using the instantaneous crankshaft angular velocity. The model reflects all essential effects on in-cylinder pressure, while the simulation time was reduced to 6 milliseconds per cycle on a standard PC. This makes it possible to estimate a cylinder-averaged pressure for each cycle up to an engine speed of more than 6000 rpm. The estimated in-cylinder pressure is available with a delay of one engine cycle.

The burned gas remaining in the cylinder after the exhaust stroke of an SI engine, i.e. the residual gas fraction, has a significant influence on both the torque production and the composition of the exhaust gas. This work investigates the behavior of the residual gas fraction over the entire operating range of the engine. A combined discrete-continuous linear model is identified, which describes the dynamic effects of the gas composition from when the gases enter the cylinder up to the measurement with a specific sensor. In this investigation, that sensor is a fast NO measurement device. The system is modelled by three elements in series: the in-cylinder mixing, the transport delay, and the exhaust mixing. The resulting model contains three elements in series connection: the in cylinder mixing, the transport delay, and the exhaust gas mixing. The model is able to calculate the fuel mass entering the cylinder during a fuel injection transient.

By comparing model and real converter performance, concise models of three-way catalyst (TWC) dynamics permit a more reliable diagnosis of converter aging than conventional approaches. Also, future model-based engine control systems should manage the state of the TWC in a way to reduce emissions. For model validation, results of transient gas concentration measurements on a dynamic test bench with an SI engine are shown. To identify the occurring fast transient phenomena, very fast multichannel gas analyzers must be used. Simulation results using a recent model of the catalytic converter are compared with actual measurements and lambda sensor readouts.

In this paper the fuel path of a sequentially injected gasoline engine is discussed. Since a fraction of the injected fuel suffers a delay due to the wall-wetting phenomenon, in transient phases a significant deviation of the air-to-fuel ratio from its setpoint can arise. The amount of fuel on the manifold wall and its rate of evaporation cannot be measured directly. Therefore, the effects of the wall-wetting on exhaust lambda and engine torque have to be considered for the identification of the dynamics. The dynamics of the exhaust-gas-oxygen (EGO) sensor is not negligible for the interpretation of the lambda measurement. Since both the dynamics and the statics of a ZrO2 Sensor are very nonlinear, a normal EGO-sensor is not suitable for these investigations. On the other hand, the engine torque is a good measure for the cylinder lambda when all other effects which lead to torque changes can be eliminated.

Modern model-based control schemes and their application on different engines need mathematical models for the various dynamic subsystems of interest. Here, the fuel path of an SI engine is investigated. When the engine speed and the throttle angle are kept constant, the fuel path is excited only by the fuel injected. Taking the NO concentration of the exhaust gas as a measure for the air/fuel ratio, models are derived for the wall-wetting dynamics, the gas mixture, as well as for the air/fuel ratio sensor. When only the spark advance is excited, the gas flow dynamics can be studied. A very fast NO measurement device is used as reference. Its time constant is below the segment time of one single cylinder (180° crank angle for a 4-cylinder engine), therefore its dynamics are much faster than the time constants of the systems investigated. A model structure considering the muliplexing effects of the discrete operation of an engine is given for the fuel path of a BMW 1.8 liter engine.

Today's engine management systems for SI engines consist of static and dynamic control algorithms. The static functions of the engine management guarantee the correct stationary operation of the engine in all the possible operating points. The static functions are contained mainly in two lookup tables, one for the spark advance and one for the metered depending on engine speed and load. Usually these lookup tables are determined with experiments on the engine test bench. In this paper, a model-based method for the evaluation of the fuel-optimal maps for spark advance and metered fuel is described. The method can be divided into several steps: 1. Measurement and identification of all the engine parameters in a reference point (including the pressure in one cylinder) Calculation of the burn-through function (progress of the combustion) Iterative calculation of the amount of residual exhaust gas Approximation of the definitive burn-through function with the Vibe equation 2.

The limiting values for NOx and HC concentrations in the exhaust gas of SI engines will be further lowered by legislation in many countries during the next years. This necessitates an improvement of the pollution control systems, which is achieved by including the dynamics of the three way catalyst into the control system. Before a control system can be designed, the dynamic behaviour of the exhaust after treatment system including the sensors has to be properly analyzed. As a first step a dynamic model of a solid-electrolyte oxygen sensor has been derived. It was the goal to obtain a better understanding of the cross sensitivities towards both reducing and oxidizing exhaust gas components such as H2, CO, O2 and NO. The model consists of three parts. Firstly, the porous protection layer, where only diffusion is assumed to occur, secondly the porous catalytic electrodes where the redox reactions take place and thirdly the solid electrolyte, where the electric potential is generated.

The selection and configuration of sensors can strongly influence the closed-loop dynamics of a system. Therefore a methodology for finding the best sensor placement is a valuable tool. This paper deals with this problem by formulating an optimization problem and applies the new method on an SI engine. The best sensor configuration is one that minimizes the overall system costs, yet still meets the system constraints. Before solving the optimization problem, the system is modeled, different sensor configurations are defined, the appropriate controller and the feedback term are developed, and the locations and size of the various errors present in the model are determined. Then, the objective function and the system constraints are defined and the optimization problem is solved considering the worst-case combination of modeling errors, which is computed using genetic algorithms. The objective function is defined as the sum of the sensor costs and of a penalty term.

In modern engine control applications, there is a distinct trend towards model-based control schemes. There are various reasons for this trend: Physical models allow deeper insights compared to heuristic functions, controllers can be designed faster and more accurately, and the possibility of obtaining an automated application scheme for the final engine to be controlled is a significant advantage. Another reason is that if physical effects can be separated, higher order models can be applied for different subsystems. This is in contrast to heuristic functions where the determination of the various maps poses large problems and is thus only feasible for low order models. One of the most important parts of an engine management system is the air-to-fuel control. The catalytic converter requires the mean air-to-fuel ratio to be very accurate in order to reach its optimal conversion rate. Disturbances from the active carbon filter and other additional devices have to be compensated.