The international marine shipping industry is responsible for the transport of around 90% of the total world trade. Low-speed two-stroke diesel engines usually propel the largest trading ships. This engine type choice is mainly motivated by its high fuel efficiency and the capacity to burn cheap low-quality fuels. To reduce the marine freight impact on the environment, the International Maritime Organization (IMO) has introduced stricter limits on the engine pollutant emissions. One of these new restrictions, named Tier III, sets the maximum NOx emissions permitted. New emission reduction technologies have to be developed to fulfill the Tier III limits on two-stroke engines since adjusting the engine combustion alone is not sufficient. There are several promising technologies to achieve the required NOx reductions, Exhaust Gas Recirculation (EGR) is one of them. For automotive applications, EGR is a mature technology, and many of the research findings can be used directly in marine applications. However, there are some differences in marine two-stroke engines, which require further development to apply and control EGR. The number of available engines for testing EGR controllers on ships and test beds is low due to the recent introduction of EGR. Hence, engine simulation models are a good alternative for developing controllers, and many different engine loading scenarios can be simulated without the high costs of running real engine tests. The primary focus of this thesis is the development and validation of models for two-stroke marine engines with EGR. The modeling follows a Mean Value Engine Model (MVEM) approach, which has a low computational complexity and permits faster than real-time simulations suitable for controller testing. A parameterization process that deals with the low measurement data availability, compared to the available data on automotive engines, is also investigated and described. As a result, the proposed model is parameterized to two different two-stroke engines showing a good agreement with the measurements in both stationary and dynamic conditions. Several engine components have been developed. One of these is a new analytic in-cylinder pressure model that captures the influence of the injection and exhaust valve timings without increasing the simulation time. A new compressor model that can extrapolate to low speeds and pressure ratios in a physically sound way is also described. This compressor model is a requirement to be able to simulate low engine loads. Moreover, a novel parameterization algorithm is shown to handle well the model nonlinearities and to obtain a good model agreement with a large number of tested compressor maps. Furthermore, the engine model is complemented with dynamic models for ship and propeller to be able to simulate transient sailing scenarios, where good EGR controller performance is crucial. The model is used to identify the low load area as the most challenging for the controller performance, due to the slower engine air path dynamics. Further low load simulations indicate that sensor bias can be problematic and lead to an undesired black smoke formation, while errors in the parameters of the controller flow estimators are not as critical. This result is valuable because for a newly built engine a proper sensor setup is more straightforward to verify than to get the right parameters for the flow estimators.

Control systems have come to play an important role in the performance of modern vehicles with regards to meeting goals on low emissions and low fuel consumption. To achieve these goals, modeling, simulation, and analysis have become standard tools for the development of control systems in the automotive industry. Modeling and Control of Engines and Drivelines provides an up-to-date treatment of the topic from a clear perspective of systems engineering and control systems, which are at the core of vehicle design. This book has three main goals. The first is to provide a thorough understanding of component models as building blocks. It has therefore been important to provide measurements from real processes, to explain the underlying physics, to describe the modeling considerations, and to validate the resulting models experimentally. Second, the authors show how the models are used in the current design of control and diagnosis systems. These system designs are never used in isolation, so the third goal is to provide a complete setting for system integration and evaluation, including complete vehicle models together with actual requirements and driving cycle analysis. Key features: Covers signals, systems, and control in modern vehicles Covers the basic dynamics of internal combustion engines and drivelines Provides a set of standard models and includes examples and case studies Covers turbo- and super-charging, and automotive dependability and diagnosis Accompanied by a web site hosting example models and problems and solutions Modeling and Control of Engines and Drivelines is a comprehensive reference for graduate students and the authors’ close collaboration with the automotive industry ensures that the knowledge and skills that practicing engineers need when analysing and developing new powertrain systems are also covered.

The book presents a complete new methodology for the on-board measurements and modeling of gas concentrations in turbocharged diesel engines. It provides the readers with a comprehensive review of the state-of-art in NOx and lambda estimation and describes new important achievements accomplished by the author. These include: the online characterization of lambda and NOx sensors; the development of control-oriented models of lambda and NOx emissions; the design of computationally efficient updating algorithms; and, finally, the application and evaluation of the methods on-board. Because of its technically oriented approach and innovative findings on both control-oriented algorithms and virtual sensing and observation, this book offers a practice-oriented guide for students, researchers and professionals working in the field of control and information engineering.

Internal combustion engines still have a potential for substantial improvements, particularly with regard to fuel efficiency and environmental compatibility. These goals can be achieved with help of control systems. Modeling and Control of Internal Combustion Engines (ICE) addresses these issues by offering an introduction to cost-effective model-based control system design for ICE. The primary emphasis is put on the ICE and its auxiliary devices. Mathematical models for these processes are developed in the text and selected feedforward and feedback control problems are discussed. The appendix contains a summary of the most important controller analysis and design methods, and a case study that analyzes a simplified idle-speed control problem. The book is written for students interested in the design of classical and novel ICE control systems.

The international marine shipping industry is responsible for the transport of around 90% of the total world trade. Low-speed two-stroke diesel engines usually propel the largest trading ships. This engine type choice is mainly motivated by its high fuel efficiency and the capacity to burn cheap low-quality fuels. To reduce the marine freight impact on the environment, the International Maritime Organization (IMO) has introduced stricter limits on the engine pollutant emissions. One of these new restrictions, named Tier III, sets the maximum NOx emissions permitted. New emission reduction technologies have to be developed to fulfill the Tier III limits on two-stroke engines since adjusting the engine combustion alone is not sufficient. There are several promising technologies to achieve the required NOx reductions, Exhaust Gas Recirculation (EGR) is one of them. For automotive applications, EGR is a mature technology, and many of the research findings can be used directly in marine applications. However, there are some differences in marine two-stroke engines, which require further development to apply and control EGR. The number of available engines for testing EGR controllers on ships and test beds is low due to the recent introduction of EGR. Hence, engine simulation models are a good alternative for developing controllers, and many different engine loading scenarios can be simulated without the high costs of running real engine tests. The primary focus of this thesis is the development and validation of models for two-stroke marine engines with EGR. The modeling follows a Mean Value Engine Model (MVEM) approach, which has a low computational complexity and permits faster than real-time simulations suitable for controller testing. A parameterization process that deals with the low measurement data availability, compared to the available data on automotive engines, is also investigated and described. As a result, the proposed model is parameterized to two different two-stroke engines showing a good agreement with the measurements in both stationary and dynamic conditions. Several engine components have been developed. One of these is a new analytic in-cylinder pressure model that captures the influence of the injection and exhaust valve timings without increasing the simulation time. A new compressor model that can extrapolate to low speeds and pressure ratios in a physically sound way is also described. This compressor model is a requirement to be able to simulate low engine loads. Moreover, a novel parameterization algorithm is shown to handle well the model nonlinearities and to obtain a good model agreement with a large number of tested compressor maps. Furthermore, the engine model is complemented with dynamic models for ship and propeller to be able to simulate transient sailing scenarios, where good EGR controller performance is crucial. The model is used to identify the low load area as the most challenging for the controller performance, due to the slower engine air path dynamics. Further low load simulations indicate that sensor bias can be problematic and lead to an undesired black smoke formation, while errors in the parameters of the controller flow estimators are not as critical. This result is valuable because for a newly built engine a proper sensor setup is more straightforward to verify than to get the right parameters for the flow estimators.