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The modern engine development process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. It is expected that predictive simulation tools that encompass the entire powertrain can potentially improve the efficiency of the calibration process. The testing of an ECU using a HiL system requires a real-time model. Additionally, if the initial parameters of the ECU are to be defined and tested, the model has to be more accurate than is typical for ECU functional testing. It is possible to enhance the generalization capability of the simulation, with neuronal network sub-models embedded into the architecture of a physical model, while still maintaining real-time execution. This paper emphasizes the experimental investigation and physical modeling of the port fuel injected SI engine.

Common rail fuel injection is the latest breakthrough in diesel engine technology. For research, development and quality control of the used electronic control units (ECU's), hardware-in-the-loop-simulation (HIL) is a useful tool for test and verification. The paper describes a high resolution real-time model of pump, rail, control valve and injectors which results in a reliable approximation of the dynamic characteristic of pressure and mass flow of the fuel. In respect to computing time and system resources this model is combined with a steady state model of a Diesel engine. It describes mainly the effects of the input variables start of injection and injection time on the engine torque at the operating point of the engine, defined by its speed and intake manifold pressure. The theoretical deduction will be completed by simulation results of the transient behavior of a Common Rail engine which are simulated by the real-time simulator CARTS(1) (Figure 1) connected to a Diesel-ECU.

The paper presents a new method based on neural networks to model the dynamic behavior of combustion pressure in SI engine cylinders, represented only by conventional input-output data. The approach is based on a functional representation of the pressure curve. The function parameters are adjusted by training a static neural network (SNN) for each working cycle. These parameters resp. “weights” are used in the following as reference pressure feature sequences. The sequences are simulated using time delay neural network (TDNN) as functions of engine speed, manifold pressure, ignition time and A/F ratio. The developed models can be used as stand alone models or as submodels within a global structure. It can be integrated as a real-time model in a HIL simulator to stimulate an ECU or implemented within an ECU for torque estimation. Performance of the proposed modeling strategy is verified by comparing experimental data from a test bench to real-time simulation results.

Real-time models, which reflect dynamic behavior of the SI engine, are needed for building up ECU testing devices like HIL simulators. In this paper the thermodynamic processes are reduced to some basic assumptions and combined with neural approximators of testbench data. So the parameters of the approximators can be easily adapted to similar new engines, while the principle structure describing interaction of the time- and angle-based processes remains unchanged. The model has been implemented and tested in a HIL-simulator. The performance of the proposed modeling strategy could be proved by comparing measurement data from a test bench to real-time simulation results.

SI engines are dynamic systems with highly nonlinear characteristics which are controlled by ECUs performing complex control algorithms. Hardware-in-the-Loop (HIL) simulation is an important tool to support test and verification during the development phase. The simulation model has to accurately reflect the dynamic behavior of the SI engine in the whole operating area. This paper describes a neural network approach to identify, i.e. to model a nonlinear dynamic system, the SI engine, represented only by I/O measurement data. The neural models have advantages with respect to robustness and measuring extent. They can be used as stand alone models or as sub-models integrated in a global model based on a physical structure. Measurements from a test bench compared to real-time simulation results prove the performance of the proposed modeling strategy.

Today diesel engines are controlled by electronic control units (ECU's), performing complex functions. New developed control algorithms should already be tested under real-time conditions in the laboratory before they are applied to the real engine. Hardware-in-the-loop simulation (HIL) is a powerful tool for development and test of the control algorithms implemented in the ECU's. Modern diesel ECU's are able to react to rotary oscillations of the crankshaft within a work cycle in order to control idling and running smoothness by a cylinder-individual variation of the start of delivery and the injection time. As a consequence also the simulator has to be able to generate torque oscillations with a resolution adapted to the sample rate of the ECU. The paper describes a high resolution real-time model which was designed by expanding a steady state model by a parallel thermodynamic model with a simplified structure.

Advanced automatic transmissions are controlled by electronic control units (ECUs) which perform the gear change and furthermore a lot of complex control and diagnostic functions. Hardware-in-the-loop simulation (HIL) is a powerful tool to develop and test the control algorithms implemented in the ECUs. The simulation is based upon mathematical models of the different power train components, adapted to the real-time requirements. In this paper an improved real-time model of an automatic transmission with a Ravigneaux planetary gear train is presented. This transmission contains among other components numerous clutches and brakes. The time-variant states of these nonlinear elements (sliding and static friction) lead to numerical problems with respect to real-time simulation. In the suggested model the transferred torques of the different clutches and brakes are calculated using the methods of multi-variable control.

Advanced passenger car control is based on multiple electronic control units (ECUs), performing complex control algorithms and diagnostic functions for the different power train components like combustion engine, automatic transmission, brakes or chassis. For research and development the engineers need tools to test and verify either the reliability of new control strategies implemented in the ECUs or the interaction of different ECUs in a car. These tests should be done to the highest extent in the laboratory to reduce costs and risks which are involved in test stands or experimental cars. Therefore the optimal solution is the operation of the ECUs in a real-time closed loop environment.

In the research project “New Exhaust Emission Concept”, supported by the Minister of Research and Technology of the Federal Republic of Germany, the main goal was the minimization of the raw exhaust emission of a gasoline engine by operating the engine in its (HC +NOx)-minimum, as well as the improvement of its fuel economy. With respect to these goals it was necessary to develop new control strategies. To preadjust the appropriate electronic control systems already in the laboratory, a real time simulator was developed, to reproduce the sensor signals of a gasoline engine under steady state, but also under dynamic conditions. The paper gives a survey over the technical specifications of the simulator, the method of mathematical engine modelling, the simulation algorithms and the used hardware solution. Some characteristic results show the performance and the adventages of the use of the simulation system.