Hybrid Dynamic Modelling of Engine Emissions on Multi-Physics Simulation Platform. A Framework Combining Dynamic and Statistical Modelling to Develop Surrogate Models of System of Internal Combustion Engine for Emission Modelling

The data-driven models used for the design of powertrain controllers are typically based on the data obtained from steady-state experiments. However, they are only valid under stable conditions and do not provide any information on the dynamic behaviour of the system. In order to capture this behaviour, dynamic modelling techniques are intensively studied to generate alternative solutions for engine mapping and calibration problem, aiming to address the need to increase productivity (reduce development time) and to develop better models for the actual behaviour of the engine under real-world conditions. In this thesis, a dynamic modelling approach is presented undertaken for the prediction of NOx emissions for a 2.0 litre Diesel engine, based on a coupled pre-validated virtual Diesel engine model (GT- Suite ® 1-D air path model) and in-cylinder combustion model (CMCL ® Stochastic Reactor Model Engine Suite). In the context of the considered Engine Simulation Framework, GT Suite + Stochastic Reactor Model (SRM), one fundamental problem is to establish a real time stochastic simulation capability. This problem can be addressed by replacing the slow combustion chemistry solver (SRM) with an appropriate NOx surrogate model. The approach taken in this research for the development of this surrogate model was based on a combination of design of dynamic experiments run on the virtual diesel engine model (GT- Suite), with a dynamic model fitted for the parameters required as input to the SRM, with a zonal design of experiments (DoEs), using Optimal Latin Hypercubes (OLH), run on the SRM model. A response surface model was fitted on the predicted NOx from the SRM OLH DoE data. This surrogate NOx model was then used to replace the computationally expensive SRM simulation, enabling real-time simulations of transient drive cycles to be executed. The performance of the approach was validated on a simulated NEDC drive cycle, against experimental data collected for the engine case study. The capability of methodology to capture the transient trends of the system shows promising results and will be used for the development of global surrogate prediction models for engine-out emissions

Model-based Calibration of Engine Control Units Using Gaussian Process Regression

Reducing the number of tests on vehicles is one of the most important requirements for increasing cost efficiency in the calibration process of engine control units (ECU). Here, employing virtual vehicles for a model-based calibration of ECUs is essential. Modelling components for virtual vehicles can be a tedious and time-consuming task. In this context, data-based modelling techniques can be an attractive alternative to physical models to increase efficiency in the modelling process. Data-based models can incorporate unknown nonlinearities encoded in the sampled data, resulting in more accurate models in practice. In combination with automated measurement, data-based modelling can help to significantly accelerate the calibration process. Furthermore, the fast simulation speed of the resulting models allows their implementation into real-time simulation environments, such as Hardware-in-the-Loop (HiL) systems, and thus enables a model-based calibration of the related ECU software function. However, generating appropriate data for learning dynamic models, i.e., the transient Design of Experiments (DoE), is not straightforward, since system boundaries and permissible excitation frequencies are not known beforehand. Thus the training data of the system measurement will be inconsistent and the main challenge of the identification process is to deal with this data to achieve a globally valid model. Furthermore, when dealing with dynamic systems in an automotive context, the Engine Control Unit typically changes operating modes while driving. Thus nonlinearities and changes of physical structures appear, which need to be considered in the model. In this thesis, a modelling system called the Local Gaussian Process Regression (LGPR), is used and adapted in order to receive a flexible modelling approach, which allows an iterative modelling process and obtains robust and globally valid dynamic models. The adapted LGPR approach is employed for the ECU calibration of dynamical automotive systems, which is critical regarding system excitation. Using LGPR, it is possible to measure the system iteratively while exploring the relevant state-space regions and improving the quality of the model step by step. The results show that LGPR is beneficial for iterative modelling of dynamical systems. Compared to the traditional Gaussian Process Regression (GPR) modelling approach, LGPR yields better results regarding the variable system dynamics

Neural Network System Identification and Controlling of Multivariable System

Most of the industrial processes are multivariable in nature. Here Greenhouse system is considered which is the important application in agricultural process. Greenhouse is to improve the environmental conditions in which plants are grown .In this paper we have proposed identification of greenhouse system using input and output data sets to estimate the best model and validate the model. For MIMO systems, Neural Network System identification provides a better alternative to find their system transfer function. The results were analyzed and the model is obtained. From this obtained model ,the system is controlled by conventional method. By these method we can identify the model and control the complicated systems like Greenhouse

Modelling and Control of Chemical Processes using Local Linear Model Networks

Recently, technology and research in control systems have made fast progress in numerous fields, such as chemical process engineering. The modelling and control may face some challenges as the procedures applied to chemical reactors and processes are nonlinear. Therefore, the aim of this research is to overcome these challenges by applying a local linear model networks technique to identify and control temperature, pH, and dissolved oxygen. The reactor studied exhibits a nonlinear function, which contains heating power, flow rate of base, and the flow rate of air as the input parameters and temperature, pH, and dissolved oxygen (pO2) the output parameters. The local linear model networks technique is proposed and applied to identify and control the pH process. This method was selected following a comparison of radial basis function neural networks (RBFNN) and adaptive neuro-fuzzy inference system (ANFIS). The results revealed that local linear model networks yielded less mean square errors than RBFNN and ANFIS. Then proportional-integral (PI) and local linear model controllers are implemented using the direct design method for the pH process. The controllers were designed on the first order pH model with 4 local models and the scaling factor is 20. Moreover, local linear model networks are also used to identify and control the level of dissolved oxygen. To select the best method for system identification, a gradient descent learning algorithm is also used to update the width scaling factor in the network, with findings compared to the manual approach for local linear model networks. However, the results demonstrated that manually updating the scaling factor yielded less mean square error than gradient descent. Consequently, PI and local linear model controllers are designed using the direct design method to control and maintain the dissolved oxygen level. The controllers were designed on first and second order pO2 model with 3 local models and the scaling factor is 20. The results for the first order revealed good control performance. However, the results for second order model lead to ringing poles which caused an unstable output with an oscillation in the input. This problem was solved by zero cancellation in the controller design and these results show good control performance. Finally, the temperature process was identified using local linear model networks and PI and local linear model controllers were designed using the direct design method. From the results, it can be observed that the first order model gives acceptable output responses compared to the higher order model. The control action for the output was behaving much better on the first order model when the number of local models M=4, compared with M=3 and M=5. Furthermore, the results revealed that the mean square error became less when the number of local models M=4 in the controller, compared with having number of local models M=3 and M=5

Emission Modelling and Model-Based Optimisation of the Engine Control

Modern Diesel engines require a model based optimisation of the engine control to fully exploit the additional degrees of freedom of modern engines. For identification of combustion engines, different experimental model structures are presented and compared to each other. The local adaptive model approach LOPOMOT is derived from the the local linear model approach LOLIMOT and an adaptive polynomial approach. Further regarded model structures are the in automotive industry well known look-up tables and the individual approximators kernel models. The model structures are generally presented and are rated with regard to applications in an electronic control unit. For the identification of the combustion engine, the combustion outputs NOx, soot and the engine torque are regarded. Experimental models are presented for measurements from the engine test bed. Stationary and dynamic effects are modelled separately, to avoid the influence of measurement dynamics. Thus, stationary measurements can be applied to identify the combustion models. The connection of these stationary combustion models to a dynamic air path model enables a dynamic overall simulation of the Diesel engine. The stationary and the dynamic model qualities are demonstrated using measurements from the engine test bed. The models are then applied for a stationary and a dynamic optimisation of control functions for the engine control unit. At first a local optimisation is presented for the stationary optimisation, which shows the Pareto front of the emissions NOx and soot. The subsequent global optimisation minimises the fuel consumption over a test cycle and formulates the emission limits as constraints. Initial values for the global optimisation are taken from the results of the local optimisation. Finally, a robust global optimisation is presented, which regards model uncertainties and variations due to series tolerances. For the dynamic optimisation, the trajectories of the air path actuators are optimised for a typical acceleration event. Because of the high computationally effort, such an optimisation can not be performed during engine operation, but it enables conclusions about suitable control structures. Thereafter, a smoke limitation based on the soot model is presented. This model based smoke limitation requires no additional calibration effort, but the model parameters are difficult to interpret. Therefore, a simplification to an open loop control structure with look-up tables is shown, which enables a manual fine tuning of the maps. This dissertation contributes to the model based optimisation of engine control functions and presents new modelling and optimisation approaches. Furthermore, new model structures are compared to the in automotive industry well known look-up tables and assets and drawbacks are discussed

Proceedings. 25. Workshop Computational Intelligence, Dortmund, 26. - 27. November 2015

Dieser Tagungsband enthält die Beiträge des 25. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) , der vom 26. – 27. November 2015 in Dortmund stattfindet

Proceedings. 24. Workshop Computational Intelligence, Dortmund, 27. - 28. November 2014

Dieser Tagungsband enthält die Beiträge des 24. Workshops "Computational Intelligence" des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA), der vom 27. - 28. November 2014 in Dortmund stattgefunden hat. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen Anwendungen und Benchmark-Problemen

Zwei-Freiheitsgrade-Struktur zur robusten Radschlupfregelung für Antiblockiersysteme

Die Regelung des gebremsten Rades eines gummibereiften Fahrzeugs stellt seit vier Jahrzehnten Generationen von Ingenieuren vor schwierige Herausforderungen und es wurden bereits zahlreiche Ansätze dazu erdacht und implementiert. Eine wesentliche Herausforderung der Regelstrecke ist die mit der Fahrzeuggeschwindigkeit skalierte Dynamik, wenn der Radschlupf als relative Geschwindigkeitsdifferenz zwischen Fahrzeug und Radaufstandspunkt geregelt werden soll, sowie die hohe Nichtlinearität der Reifenkraftschlusskennlinie in Abhängigkeit des Schlupfes. In der tatsächlichen Implementierung kommt darüber hinaus noch der Abtastcharakter der Regelung auf einem digitalen Mikrocontroller hinzu, der im Rahmen dieser Arbeit systematisch in den Entwurf mit einbezogen werden soll. Dazu wird zuerst ein physikalisches Modell des Fahrzeugs einschließlich der Bremsenaktorik aufgestellt und dieses anschließend mittels Strukturmaßen untersucht sowie Schlüsse für die notwendige Reglerstruktur aus dieser Untersuchung abgeleitet. In dieser Arbeit wird ein modellbasierter Ansatz zur Regelung des Radschlupfes vorgeschlagen, der aus einer Zwei-Freiheitsgrade-Struktur mit nichtlinearer modellbasierter Vorsteuerung und einer robust entworfenen Rückführung mittels Gain-Scheduling besteht. Für die Vorsteuerung wird ein Ansatz über die exakte Eingangs-/Ausgangslinearisierung gewählt, mit dem sich das nichtlineare System bezogen auf das Ein-/Ausgangsverhalten wie ein lineares System regeln lässt. Für die Rückführung wird ein Gain-Scheduling über die Schedulingparameter Fahrzeuggeschwindigkeit und Radschlupf durchgeführt, um den durch die hohe Parameterunsicherheit in der Reifenkennline und die reziproke Abhängigkeit der Systemdynamik von der Geschwindigkeit variablen Parameterbereich in kleinere Unsicherheitsbereiche zu unterteilen, für die anschließend ein linearer Regler mit fester Struktur über die Methode der robusten Polbereichsvorgabe entworfen wird. Basierend auf diesem Schlupfregler wird in einem zweiten Schritt ein Algorithmus verwendet, der in der Lage ist, das Maximum der Reibwertkennlinie einzuregeln, um den verfügbaren Kraftschluss bestmöglich auszunutzen, das sog. Extremum Seeking. Der gesamte Reglerentwurf erfolgt dabei rein zeitdiskret, um die charakteristischen Effekte der Diskretisierung bei einer digitalen Regelung behandeln zu können. Die vorgeschlagene Reglerstruktur wird dabei in Simulationen für unterschiedliche Reibwerte der modellbasierten Vorsteuerung und der realen Strecke untersucht und dabei gezeigt, dass die Regelung mit Extremwertsuche auch in der Lage ist, das Maximum zu finden, wenn die Reibwertkurve ihr Maximum verändert

Global Nonlinear Modeling Using Automated Local Model Networks in Real Time

Global nonlinear modeling is a challenging task that spans multiple disciplines. When it is necessary to develop a model across the global input space, and a single linear model is insufficient, nonlinear modeling methods are required. If the model is constrained to be developed autonomously in real time, the modeling problem is more difficult, and there are fewer available resources, tools, and techniques for efficient and effective model development. This scenario specifically arises in the context of the NASA Learn-to-Fly concept, which aims to develop tools for real-time aerodynamic modeling and control for new or modified flight vehicles, and which serves as the motivation for this research. This work aims to develop a modeling method that enables the model to be developed automatically in real time, with limited prior knowledge required, and that provides a model that is easily interpretable, allows physical insight into the system, and offers good global and local prediction capabilities. A novel method is developed and presented in this work for automated real-time global nonlinear modeling using local model networks, known as Smoothed Partitioning with LocalIzed Trees in Real time (SPLITR). The global nonlinear system behavior is partitioned into several local regions known as cells, with the dimension, location, and timing of each partition automatically selected based on a new residual characterization procedure, under the constraints of real-time operation. Regression trees represent the successive partitioning of the global input space and describe the evolution of the cell structure. Recursive equation-error least-squares parameter estimation in the time domain is used to estimate a model that represents the local system behavior in each region so that the model can be updated independently with data in the explanatory variable ranges of each cell, even if the data are not contiguous in time. A weighted superposition of these piecewise local models across the input space forms a global nonlinear model that also accurately captures the local behavior. The SPLITR approach was tested and validated using both simplified simulated test data, as well as experimental flight test data, and the results were analyzed in terms of model predictive capabilities and interpretability. The results show that SPLITR can be used to automatically partition complex nonlinear behavior in real time, produce an accurate model, and provide valuable physical insight into the local and global system behavior

Proceedings - 28. Workshop Computational Intelligence, Dortmund, 29. - 30. November 2018

Dieser Tagungsband enthält die Beiträge des 28. Workshops Computational Intelligence. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen