Kai Hoffmann

Combustion model reduction for diesel engine control design

Abstract The subject of this work is the derivation of a simulation model for premixed charge compression ignition (PCCI) combustion that can be used in closed-loop control development. For the high-pressure part of the engine cycle, a detailed three-dimensional computational fluid dynamics model is reduced to a stand-alone multi-zone chemistry model. This multi-zone chemistry model is extended by a mean value model accounting for the gas exchange losses. The resulting model is capable of describing PCCI combustion with stationary exactness, and is at the same time very economic with respect to computational costs. The model is further extended by the identified system dynamics that influence the stationary inputs. For this purporse, a Wiener model is set up that uses the stationary model as a non-linear system representation. In this way, a dynamic non-linear model for the representation of the controlled plant diesel engine is created.

A multi-zone combustion model with detailed chemistry including cycle-to-cycle dynamics for diesel engine control design

This paper reviews the research activities within the subproject B1 Model Reduction for Low-Temperature Combustion Processes through CFD-Simulations and Multi-Zone Models of the Collaborative Research Centre SFB 686 – Model-Based Control of Homogenized Low-Temperature Combustion. The SFB 686 is carried out at RWTH Aachen University, Germany and Bielefeld University, Germany, and is funded by the German Research Foundation (DFG). This paper thereby summarizes the outcome of various publications by the authors, with the appropriate references given in the individual sections. Additionally, some new results are introduced. The particular subject of this work is a dynamic simulation strategy for premixed charge compression ignition (PCCI) combustion that can be used in closed-loop control development. A detailed multi-zone chemistry model for the high-pressure part of the engine cycle is extended by a mean value gas exchange model accounting for the low-pressure part. Thus, an efficient model capable of describing PCCI combustion is sufficiently well established. In order to capture cycle-to-cycle dynamics, identified system dynamics influencing the input parameters are incorporated. For this, a Wiener model is set up that uses the combustion model as a nonlinear system representation. In this way, a dynamic nonlinear model for the representation of the controlled plant Diesel engine is created. The model is validated against transient experimental engine data.

Model-Based Optimal Control for PCCI Combustion Engines

Abstract New combustion methods for engines have been recently researched very intensively. In diesel engines, the homogenisation of the air-fuel mixture by early fuel injection has significant effects on emission reduction. The paper presents a model-based optimal control strategy for premixed charge compression ignition (PCCI) low temperature combustion in diesel engines. In order to understand the basic properties of the PCCI mode, static and dynamic measurements were conducted using a real conventional diesel engine. The main inputs of the combustion process are the exhaust gas recirculation rate and injection parameters. Outputs are the indicated mean effective pressure and the fuel mass conversion balance point. The process has very fast, almost proportional dynamics over the engines working cycles. Focusing on the static behaviour of the process, a nonlinear neural network model is used for identification. Successive linearisation of the nonlinear network is used as predictive controller model. The presented controller structure is able to consider constraints and can be computed very fast. Finally, the controller is validated under real time conditions by experimental tests at the engine test bench. Although the controller structure contains a model and a convex optimisation step with regards to constraints, its implementation is very simple, as no observer is used, and the linearised model consists of static gains only.

Aspects of gasoline controlled auto ignition — development of a controller concept

A promising approach to decrease emissions and fuel consumption at the same time for gasoline engines is the controlled auto ignition. The complex process is analysed and modelled within the “Collaborate Research Centre 686 — model based control of the homogenized low temperature combustion” by the Institute for Combustion Engines and the Institute of Automatic Control at RWTH Aachen University to design a future controller concept.

Control of future low Temperature Combustion Technologies with nonlinear Model based Predictive Control based on Neural Networks

Abstract The combustion in future engines will work with a very high amount of recirculated exhaust gas in part load conditions to enable a low peak combustion temperature. This combustion suffers from instabilities of the process and a highly nonlinear behaviour. The paper presents the use of neural nets for observing the engine. A nonlinear model without feedback of measurements is linearised online and combined with an extended Kalman filter. This observer is compared to a neural net with observer structure by application to two different valve timing strategies. The more promising observer is combined with a model based predictive controller with a quadratic cost function. Its analytic solution is compared with quadratic programming for respecting constraints in the prediction for improving the control error.

Non-linear Model-based Predictive Control with Constraints for Controlled Auto-Ignition

Abstract Combustion with a high amount of recirculated exhaust gas is increasingly gaining interest. Such in part load conditions a low combustion peak temperature can be achieved which yields lowest emissions but suffers from instabilities of the process and a highly non-linear behavior. These properties make a closed-loop control a requirement for transient operation but also a challenge. The paper presents an innovative non-linear Model-based Predictive Controller (NMPC) for controlling the indicated mean effective pressure (IMEP) and crank angle of 50% released heat (CA50) while accounting for constraints on the maximum pressure rise (dpmax). The implementation of the controller is presented in a framework for rapid control prototyping (RCP) that enables the user to set up a complex controller for transient operation in a short time frame. The ability of the approach is proved by application to the real engine.

NEURAL NETWORKS FOR MODELLING AND CONTROLLING FUTURE LOW TEMPERATURE COMBUSTION TECHNOLOGIES

Abstract Modern internal combustion engines with a low peak combustion temperature suffer from instabilities of the process and a highly nonlinear behaviour. These make a closed loop control a necessity. In order to build and tune a controller a model is needed, which has to be able to reproduce the nonlinear behaviour. The paper presents the application of offline trained NNSSIF nets, a neural networks architecture with state space attributes. These are combined with an extended Kalman filter and a nonlinear model-based predictive controller to a research internal combustion engine.

Aspekte der ottomotorischen Selbstzündung

Ein viel versprechender Ansatz, sowohl Schadstoffausstos als auch Kraftstoffverbrauch bei Ottomotoren zu senken, stellt der Einsatz der kontrollierten Selbstzundung dar. Der komplexe Prozess wird im Rahmen des „Sonderforschungsbereichs 686 — Modellbasierte Regelung der homogenisierten Niedertemperatur-Verbrennung“ vom Lehrstuhl fur Verbrennungskraftmaschinen und dem Institut fur Regelungstechnik der RWTH Aachen University analysiert und modelliert, um ein kunftiges Regelkonzept zu entwerfen.Ein viel versprechender Ansatz, sowohl Schadstoffausstos als auch Kraftstoffverbrauch bei Ottomotoren zu senken, stellt der Einsatz der kontrollierten Selbstzundung dar. Der komplexe Prozess wird im Rahmen des „Sonderforschungsbereichs 686 — Modellbasierte Regelung der homogenisierten Niedertemperatur-Verbrennung“ vom Lehrstuhl fur Verbrennungskraftmaschinen und dem Institut fur Regelungstechnik der RWTH Aachen University analysiert und modelliert, um ein kunftiges Regelkonzept zu entwerfen.