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In this paper we present an advanced knock detection approach. The detection concept consists of a two-level feature extraction step followed by neural network detector. A knock tendency index is estimated that takes into account the statistical behavior of the knock phenomena. The configuration of the neural network is based on a signal database that was acquired under almost ‘on-road’ conditions. The experimental set-up consisted of several measurement sessions in a special vehicle test cell. In order to achieve a most realistic knock database the test engine was mounted on an in-production car.

In the scope of a research collaboration, ITT Automotive Europe and Darmstadt University of Technology are developing control strategies for a low-cost Brake-by-Wire system. However, since there is a wide range of variation in the efficiency of the gear units used in electromechanical brakes, this becomes a demanding task. The paper first describes the assembly and operation of ITT's early generation brake actuator. It introduces a model of the electromechanical brake with its structure and subsystems as a major tool in the development process. A detailed analysis of the signals, already available from the brake and the vehicle, is discussed for their advantages and disadvantages with regard to a possible use in the controller design. Different approaches for clamping-force, peripheral-force and brake-torque sensing are compared. An integrated clamping force sensor for feedback control of prototype actuators was developed.

Faced with the need to reduce development time and cost, the hardware-in-the-loop (HIL) simulation increasingly proves to be an efficient tool in the automotive industry. It offers the possibility to investigate new engine control systems with fewer expensive engine dynamometer experiments and test drives. In the scope of a research collaboration, Daimler Benz and Darmstadt University of Technology are developing a hardware-in-the loop simulator for the investigation of the electronic engine management of the new Mercedes Benz truck engine series 500 and 900. This paper first describes the necessary models for real-time simulation of the subsystems Diesel engine, turbo charger and vehicle. Then the setup of the simulator test bench is introduced and the performance of the simulator is demonstrated by several experimental results.

This paper deals with physical modelling of a turbocharger with variable turbine geometry (VTG) and its real-time simulation based on dynamic artificial neural networks (ANN). Thermodynamic und fluiddynamic equations, describing the basic functionality and relations between pressure, mass flow and temperature at the inlet and outlet ports of compressor and turbine, build up a multiple input multiple output model (MIMO). A special kind of ANN, namely the LoLiMoT algorithm, is used for real-time simulation. Training the network using measurement and simulated data, the dynamic behaviour can be simulated with less computational effort than the physical model. The neural network may be used in engine control systems as observer for non measurable signals, like rotor speed or turbine and compressor torque, figure 1.

The use of model based approaches in areas such as simulation, control design, optimization, etc. is crucial for the development of highly sophisticated systems. This is especially true for typically very tight time-to-market frames. Physical modeling of IC engine emissions based on first principles is extremely complex and still requires by far too much calculation time. However, special fast neural networks represent a promising alternative for an accurate modeling of the emission behavior, even for dynamic conditions. This paper first describes the process of developing dynamic neural emission models. The required data is collected by a specially designed dynamic measurement strategy. The models themselves are then used for the optimization of the dynamic engine behavior concerning consumption, emissions and drivability.

In comparison to other vehicles motorcycles have very special driving characteristics, so the typical use of motorcycles is clearly distinct from the use of passenger cars. At Darmstadt University the riding behavior of motorcyclists has been experimentally investigated [2, 3, 4, 5], especially in order to determine their exhaust and noise emissions in real traffic. The results and the essential differences between motorcycles and cars should be considered in the discussion of testing methods and limiting values, e.g., for exhaust and noise emissions of two-wheelers. This paper presents a comparison between the typical driving performance of motorcycles and passenger cars and contains results of motorcycle exhaust and noise emission measurements in real traffic and in statutory tests. The current legal measuring standards are found not to represent the reality of motorcycle traffic in a sufficient manner.

In the scope of a research collaboration, Continental Teves (formerly ITT Automotive Europe) and Darmstadt University of Technology are developing control strategies for a low-cost Brake-by-Wire system, using no clamping-force or brake-torque sensor as feedback [1]. However, since there is a wide range of variation in the efficiency of the gear units used in electromechanical brakes, this becomes a demanding task. The paper first describes the assembly and operation of Continental Teves' third generation brake actuator, which is still operated using an integrated clamping force sensor [2]. It introduces the development environment of Darmstadt University of Technology, consisting of a brake test stand, a complex brake actuator model, and a simplified brake actuator model.