After undergoing an extensive study about engine air mass flow measurement approaches as well as engine modelling for air mass flow prediction, a major problem found to exist is that engineers have still not found a suitable technique to accurately measure the air mass flow entering the cylinder of an internal combustion engine. The engine air mass flow is the most important parameter needed during engine development so the fuel control can be accurately calibrated and as a result increase performance and reduce emission output of an engine. The current methods used to determine the air mass flow lead to inaccuracies due to the large amount of mathematical assumptions and also sensor errors and as a result the mapping and calibration process of a new engine family takes approximately 2 years due to extensive modelling and testing required overcoming the above drawbacks. To improve this, the distributed lumped modelling technique (D-L) of the inlet manifold was chosen, where the intake system is separated into very small sections which are distributed continuously throughout the volume of the intake until entering the cylinder. This technique is validated against a CFD model of the engine’s intake system and real engine data as well as a 1D engine model.

To control the combustion in an engine, an accurate estimation of the air mass flow is required. Due to strict emission legislation and high demands on fuel consumption from customers, a technology called variable valve timing is investigated. This technology controls the amount of air inducted to the engine cylinder and the amount of gases pushed out of the cylinder, via the inlet and exhaust valves. The air mass flow is usually estimated by large look-up tables but when introducing variable valve timing, the air mass flow also depends on the angles of the inlet and exhaust valves causing these look-up tables to grow with two dimensions. To avoid this, models to estimate the air mass flow have been derived. This has been done with grey-box models, using physical equations together with unknown parameters estimated by solving a linear least-squares optimization problem. To be able to implement the models in the electronic control unit in the future, only sensors implemented in a commercial vehicle are used as much as possible. The work has been done using an inline 6-cylinder diesel engine with measurements from steady-state conditions. All four models derived in this project are based on the estimation methods in use today with fix cam phasing, and are derived from the ideal gas law together with a volumetric efficiency factor. The first three models derived in this work only include sensors provided in commercial engines. The measurements needed as input signals are engine rotational speed, crank angle resolved pressure in the intake manifold, intake and exhaust valve angles and intake manifold temperature. The fourth and last model is divided into three sub-models to model different parts of the four-stroke engine cycle. This model also includes crank angle resolved exhaust manifold pressure and exhaust manifold temperature, where the temperature is the only sensor used in this project that is not provided in a commercial engine. It has been concluded how influential it is to use correctly measured values for the input signals. Since the manifold pressure and the cylinder volume vary during one four-stroke cycle, it is essential that these signal measurements are taken at the right crank angle degree. With wrong crank angle degree, the estimation is worse than if the cylinder volume is constant for all operating points and the pressure signals are taken as a mean value over the whole four-stroke cycle. Further development to reach better estimation results with lower relative error is needed. However, for the work in this thesis, the model with best model fit is estimating the air mass flow well enough to use it as a basis for further control.

<p>Stricter emission legislation for heavy trucks in combination with the customers demand on low fuel consumption has resulted in intensive technical development of engines and their control systems. To control all these new solutions it is desirable to have reliable models for important control variables. One of them is the air mass-flow, which is important when controlling the amount of recirculated exhaust gases in the EGR system and to make sure that the air to fuel ratio is correct in the cylinders. The purpose with this thesis was to use system identification theory to develop a model for the air mass-flow through the compressor. First linear black-box models were developed without any knowledge of the physics behind. The collected data was preprocessed to work in the modeling procedure and then models with one or more inputs where built according to the ARX model structure. To further improve the models performance, non-linear regressors was developed from physical relations for the air mass-flow and used to form grey-box models of the air mass-flow.In conclusion, the performance was evaluated through comparing the estimated air mass-flow from the best model with the estimate that an extended Kalman filter together with a physical model produced.</p> / <p>Hårdare utsläppskrav för tunga lastbilar i kombination med kundernas efterfrågan på låg bränsleförbrukning har resulterat i en intensiv utveckling av motorer och deras kontrollsystem. För att kunna styra alla dessa nya lösningar är det nödvändigt att ha tillförlitliga modeller över viktiga kontrollvariabler. En av dessa är luftmassflödet som är viktig när man ska kontrollera den mängd avgaser som återcirkuleras i EGR-systemet och för att se till att kvoten mellan luft och bränsle är korrekt i motorns cylindrar. Syftet med det här examensarbetet var att använda systemidentifiering för att ta fram en modell över luftmassflödet förbi kompressorn. Först togs linjära svartboxmodeller fram utan att ta med någon kunskap om den bakomliggande fysiken. Insamlade data förbehandlades för att passa in i modelleringsproceduren och efter det skapades i enlighet med ARX-modellstrukturen modeller med en eller flera insignaler. För att ytterligare förbättra modellernas prestanda togs icke-linjära regressorer fram med hjälp av fysikaliska relationer för luftmassflödet. Dessa användes sedan för att skapa gråboxmodeller av luftmassflödet. Avslutningsvis utvärderades prestandan genom att det estimerade luftmassflödet från den bästa modellen jämfördes med det estimat som ett utökat kalmanfilter tillsammans med fysikaliska ekvationer genererade.</p>

Stricter emission legislation for heavy trucks in combination with the customers demand on low fuel consumption has resulted in intensive technical development of engines and their control systems. To control all these new solutions it is desirable to have reliable models for important control variables. One of them is the air mass-flow, which is important when controlling the amount of recirculated exhaust gases in the EGR system and to make sure that the air to fuel ratio is correct in the cylinders. The purpose with this thesis was to use system identification theory to develop a model for the air mass-flow through the compressor. First linear black-box models were developed without any knowledge of the physics behind. The collected data was preprocessed to work in the modeling procedure and then models with one or more inputs where built according to the ARX model structure. To further improve the models performance, non-linear regressors was developed from physical relations for the air mass-flow and used to form grey-box models of the air mass-flow.In conclusion, the performance was evaluated through comparing the estimated air mass-flow from the best model with the estimate that an extended Kalman filter together with a physical model produced. / Hårdare utsläppskrav för tunga lastbilar i kombination med kundernas efterfrågan på låg bränsleförbrukning har resulterat i en intensiv utveckling av motorer och deras kontrollsystem. För att kunna styra alla dessa nya lösningar är det nödvändigt att ha tillförlitliga modeller över viktiga kontrollvariabler. En av dessa är luftmassflödet som är viktig när man ska kontrollera den mängd avgaser som återcirkuleras i EGR-systemet och för att se till att kvoten mellan luft och bränsle är korrekt i motorns cylindrar. Syftet med det här examensarbetet var att använda systemidentifiering för att ta fram en modell över luftmassflödet förbi kompressorn. Först togs linjära svartboxmodeller fram utan att ta med någon kunskap om den bakomliggande fysiken. Insamlade data förbehandlades för att passa in i modelleringsproceduren och efter det skapades i enlighet med ARX-modellstrukturen modeller med en eller flera insignaler. För att ytterligare förbättra modellernas prestanda togs icke-linjära regressorer fram med hjälp av fysikaliska relationer för luftmassflödet. Dessa användes sedan för att skapa gråboxmodeller av luftmassflödet. Avslutningsvis utvärderades prestandan genom att det estimerade luftmassflödet från den bästa modellen jämfördes med det estimat som ett utökat kalmanfilter tillsammans med fysikaliska ekvationer genererade.

The potential of Solid Oxide Fuel Cell Gas Turbine (SOFC/GT) hybrid systems for fuel flexibility makes this technology greatly attractive for system hybridization with various fuel processing units in advanced power generation systems and/or polygeneration plants. Such hybrid technologies open up the possibility and opportunities for improvement of system reliabilities and operabilities. However, SOFC/GT hybrid systems have not yet reached their full potential in term of capitalizing on the synergistic benefits of fuel cell and gas turbine cycles. Integrating fuel cells with gas turbine and other components for transient operations increases the risk for exposure to rapid and significant changes in process dynamics and performance, which are primarily associated with fuel cell thermal management and compressor surge. This can lead to severe fuel cell failure, shaft overspeed, and gas turbine damage. Sufficient dynamic control architectures should be made to mitigate undesirable dynamic behaviours and/or system constraint violations before this technology can be commercialized. But, adequate understanding about dynamic coupling interactions between system components in the hybrid configuration is essential. Considering this critical need for system identification of SOFC/GT hybrid in fuel flexible systems, this thesis investigates the dynamic performance of SOFC/GT hybrid technology in response to fuel composition changes. Hardware-based simulations, which combined actual equipment of direct-fired recuperated gas turbine system and simulated fuel cell subsystem, are used to experimentally investigate the impacts of fuel composition changes on the SOFC/GT hybrid system, reducing potentially large inaccuracies in the dynamic study. The impacts of fuel composition in a closed loop operation using turbine speed control were first studied for the purpose of simplicity. Quantification of safe operating conditions for dynamic operations associated with carbon deposition and compressor stall and surge was done prior to the execution of experimentation. With closed loop tests, the dynamic performance of SOFC/GT hybrid technology due to a transition in gas composition could be uniquely characterized, eliminating the interactive effects of other process variables and disturbances. However, for an extensive system analysis, open loop tests (without turbine speed control) were also conducted such that potential coupling impacts exhibited by the SOFC/GT hybrid during fuel transients could be explored. Detailed characterization of SOFC/GT dynamic performance was performed to identify the interrelationship of each fuel cell variable in response to fuel composition dynamics and their contributions to operability of the system. As a result of lowering LHV content in the fuel feed, which involved a transition from coal-derived syngas to humidified methane composition in the SOFC anode, the system demonstrated a dramatic transient increase in fuel cell thermal effluent with a time scale of seconds, resulting from the conversion of fuel cell thermal energy storage into chemical energy. This transient was highly associated with the dynamics of solid and gas temperatures, heat flux, heat generation in the fuel cell due to perturbations in methane reforming, water-gas shifting, and electrochemical hydrogen oxidation. In turn, the dramatic changes in fuel cell thermal effluent resulting from the anode composition changes drove the turbine transients that caused significant cathode airflow fluctuations. This study revealed that the cathode air mass flow change was a major linking event during fuel composition changes in the SOFC/GT hybrid system. Both transients in cathode air mass flow and anode composition significantly affected the hybrid system performance. Due to significant coupling between fuel composition transitions and cathode air mass flow changes, thermal management of SOFC/GT hybrid systems might be challenging. Yet, it was suggested that modulating cathode air flow offered promise for effective dynamic control of SOFC/GT hybrid systems with fuel flexibility. / Thesis / Doctor of Philosophy (PhD)