Peter Drews

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 hybrid control approach for low temperature combustion engine control

In this paper, a hybrid control approach for low temperature combustion engines is presented. The identification as well as the controller design are demonstrated. In order to identify piecewise affine models, we propose to use correlation clustering algorithms, which are developed and used in the field of data mining. We outline the identification of the low temperature combustion engine from measurement data based on correlation clustering. The output of the identified model reproduces the measurement data of the engine very well. Based on this piecewise affine model of the process, a hybrid model predictive controller is considered. It can be shown that the hybrid controller is able to produce better control results than a model predictive controller using a single linear model. The main advantage is that the hybrid controller is able to manage the system characteristics of different operating points for each prediction step.

Evolution in Domain Model-Based Requirements Engineering for Control Systems Development

When developing software-based control systems, knowledge and experiences in the relevant domain are of great importance. Small- and medium-sized enterprises (SMEs) that are most active here need to capture requirements under severe time and costs pressures. In previous work we have shown that a domain model based on the requirements formalism i* accelerates the requirements capture. Furthermore, the domain model-based similarity search supports the detection of reusable components from earlier projects. But due to the innovativeness, flexibility, and customer-orientation of control systems development, this domain model is subject to continuous change. Within this paper, we investigate the effects of model evolution on our domain model-based requirements engineering approach. Building on examples from industrial practice, we develop a classification of possible domain model modifications. For each such class, we analyze its impact on the similarity search and derive appropriate counter measures to limit these harmful impacts.

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.

Fuel-Efficient Model-Based Optimal MIMO Control for PCCI Engines

Abstract Recent research in modern combustion technologies, like partial homogeneous charge compression ignition (PCCI), demonstrates the capability of reducing pollutant emissions, e.g. soot and NOX. In addition to this advantage, a possibility to reduce fuel consumption and noise production by model-based optimal control is presented in this paper. In order to understand the basic properties of the PCCI mode, process measurements were conducted using a slightly modified series diesel engine. Control variables are engine combustion parameters: the indicated mean effective pressure, the combustion average and the maximum gradient of the cylinder-pressure. Control inputs are the parameters: quantity of injected fuel, start of injection and the intake manifold fraction of recirculated exhaust gas. The process has very fast, almost proportional behaviour over the engines working cycles. Focusing on the static behaviour of the process, a nonlinear neural network model is used for identification. Successive linearization of the nonlinear network is used to build an affine internal controller model for the actual operating point. The presented controller structure is able to consider constraints by individual formulation of the cost function. With this configuration the closed-loop process is able to track the combustion setpoints with high control quality with minimal possible fuel consumption and combustion noise.