One method to increase efficiency, robustness and accuracy of automatic control, is to introduce mathematical models of the system in question to increase performance. With these models, it is possible to predict the behavior of the system, which enables control according to the predictions. The problem here is that if these models do not describe the dynamics of the system well enough, this method could fail to increase performance. To address this problem, one idea is to estimate the dynamics of the system during operation, using methods for system identification, signal processing and sensor fusion. In this thesis, the possibilities of estimating a ship's dynamics during operation have been investigated. The mathematical model describing the dynamics of the ship is a graybox model, which is based on the physical and mechanical relations. This model's properties are therefore described by physical quantities such as mass and moment of inertia, all of which are unknown. This means that, when estimating the model, these physical properties will be estimated. For a systematic approach, first a simulation environment with a 4-degrees-of-freedom ship model has been developed. This environment has been used for validation of system identification methods. A model of a podded propulsion system has also been derived and validated. The methods for estimating the properties of the ship have been analyzed using the data collected from the simulations. For system identification and estimation of ship properties, the influence of measurement noise and potential of detecting a change in dynamics has been analyzed. This has been done through Monte Carlo simulations of the estimation method with different noise realizations in the simulations, to analyze how the measurement noise affects the variance and bias for the estimates. The results show that variance and bias vary a lot between the parameters and that even a small change in dynamics is visible in some parameter estimates when only ten minutes of data have been used. A method based on cumulative summation (CUSUM) has been proposed and validated to analyze if such a method could yield fast and effective detection of system deviations. The results show that the method is rather effective a with robust detection of changes in the dynamics after about four minutes of data collection. Finally, the methods have been validated on data collected on a real ship to analyze the potential of the methods under actual circumstances. The results show that the particular data is not appropriate for this kind of application along with some additional problems that can yield impaired results. / Genom att inkludera matematiska modeller som beskriver ett systems dynamik i styrningsalgoritmer, kan man åstadkomma en automatisk styrning med förbättrad effektivitet, robusthet och noggrannhet. Med dessa modeller går det att förutsäga beteendet hos systemet och därmed öppnas också möjligheten att använda sig av detta i styrningen. Problemet är att om dessa modeller inte beskriver systemets dynamik tillräckligt bra kan prestandan istället sänkas genom dessa metoder. Den här sortens problem kan man lösa genom att aktivt skatta systemets dynamik under körning, med hjälp av metoder för systemidentifiering, signalbehandling och sensorfusion. I denna exjobbsrapport har möjligheterna att skatta ett skepps girdynamik undersökts. Den matematiska modell som beskriver skeppets dynamik är en grålådemodell som baserar sig på fysikaliska och mekaniska samband. Denna modells egenskaper beskrivs därför av fysikaliska storheter så som massa, tröghetsmoment och tyngdpunkt, vilka alla är okända. Detta innebär att vid modellskattning skattas dessa fysikaliska storheter, vilka kan vara av stort intresse. En simuleringsmiljö med en skeppsmodell med fyra frihetsgrader har skapats och använts för att validera metoder för systemidentifiering. En modell av ett roterbart framdrivningssystem har också härletts och inkluderats i simuleringsmodellen. Vid systemidentifiering och skattning av skeppets egenskaper har dels inverkan av mätbrus analyserats samt även möjligheter till att detektera skillnader i dynamik. Detta har gjorts med Monte Carlo-simuleringar av skattningsmetoden med olika brusrealiseringar för att analysera hur mätbrus påverkar variansen och metodfelet hos skattningarna. Resultaten visar att vissa parametrar skattas med större noggrannhet och hos dessa kan därmed en förändring i dynamik identifieras när endast tio minuter av data har använts. En metod baserad på kumulativ summering av residualer har formulerats och validerats, detta för att undersöka om en sådan metod kan ge snabb och effektiv detektion av systemförändringar. Resultat visar på robusthet i att detektera skillnader i dynamik efter ungefär fyra minuter av datainsamling. Slutligen har metoderna validerats på data insamlad på ett riktigt skepp för att undersöka potentialen under verkliga omständigheter. Resultaten visar att just denna data inte är lämplig för denna applikation samt några problem som kan leda till försämrade resultat.

The marine industry has increased its adoption of pure-electric, diesel-electric, and other non-traditional propulsion architectures to reduce ship emissions and fuel consumption. While these technologies can improve performance, the design of a propulsion system becomes challenging, given that no single technology is superior across all vessel types. Furthermore, even identical ships with different operating patterns may be better suited to different propulsion technologies. Addressing this problem, previous research has shown that if key elements of a vessel's operational pro file are known, simulation and optimization techniques can be employed to evaluate multiple propulsion architectures and result in a better propulsion system design and energy management strategy for a given vessel. While these studies have demonstrated the performance improvements that can be achieved from optimizing clean marine propulsion systems, they rely on vessel operational profiles obtained through physical measurement from existing ships. From a practical point of view, the optimization of a vessel's propulsion system needs to occur prior to a vessel's construction and thus precludes physical measurement. To this end, this thesis introduces a marine simulation platform for producing vessel operational profiles which enable propulsion system optimization during the ship design process. Core subsystem modules are constructed for simulating ship motions in 3 degrees of freedom and result in operational profile time-series, including propulsion power. Data is acquired from a benchmark vessel to validate the simulation. Results show the proposed approach strikes a balance between speed, accuracy, and complexity compared with other available tools. / Graduate