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Stanje napolnjenosti litij-titanat oksidnih hranilnikov energije
Jaka Gselman, 2024, undergraduate thesis

Abstract: V letu 2023 se je delež tržišča za shranjevanje energije potrojil; proizvajamo in porabljamo vedno več energije, ampak včasih proizvedemo več energije, kot je zmoremo porabiti ali pa premalo. Zato je potrebno energijo shraniti. Baterijske celice so zdaleč najbolj učinkovit način shranjevanja energije. Želja po dolgotrajnejših in bolj zanesljivih sistemih je začela v ospredje postavljali litij-titanat oksidne celice. Hkrati so se proizvajalci inverterjev začeli nagibati h visoko napetostim baterijskim sistemom zaradi manjših izgub ter cene. Diplomsko delo se osredotoča na visokonapetostni LTO baterijski sistem, podrobno opisuje njegovo delovanje in sestavne dele ter primerja različne metode določanja stanja napolnjenosti.
Keywords: zalogovniki električne energije, litij-titanat oksid, kalman filter, stanje napolnjenosti, SOC.
Published in DKUM: 22.10.2024; Views: 0; Downloads: 5
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Kalman filter or VAR models to predict unemployment rate in Romania?
Mihaela Simionescu, 2015, original scientific article

Abstract: This paper brings to light an economic problem that frequently appears in practice: For the same variable, more alternative forecasts are proposed, yet the decision-making process requires the use of a single prediction. Therefore, a forecast assessment is necessary to select the best prediction. The aim of this research is to propose some strategies for improving the unemployment rate forecast in Romania by conducting a comparative accuracy analysis of unemployment rate forecasts based on two quantitative methods: Kalman filter and vector-auto-regressive (VAR) models. The first method considers the evolution of unemployment components, while the VAR model takes into account the interdependencies between the unemployment rate and the inflation rate. According to the Granger causality test, the inflation rate in the first difference is a cause of the unemployment rate in the first difference, these data sets being stationary. For the unemployment rate forecasts for 2010-2012 in Romania, the VAR models (in all variants of VAR simulations) determined more accurate predictions than Kalman filter based on two state space models for all accuracy measures. According to mean absolute scaled error, the dynamic-stochastic simulations used in predicting unemployment based on the VAR model are the most accurate. Another strategy for improving the initial forecasts based on the Kalman filter used the adjusted unemployment data transformed by the application of the Hodrick-Prescott filter. However, the use of VAR models rather than different variants of the Kalman filter methods remains the best strategy in improving the quality of the unemployment rate forecast in Romania. The explanation of these results is related to the fact that the interaction of unemployment with inflation provides useful information for predictions of the evolution of unemployment related to its components (i.e., natural unemployment and cyclical component).
Keywords: forecasts, accuracy, Kalman filter, Hodrick-Prescott filter, VAR models, unemployment rate
Published in DKUM: 13.11.2017; Views: 1816; Downloads: 391
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Finite element modelling of a field-sensed magnetic suspended system for accurate proximity measurement based on a sensor fusion algorithm with Unscented Kalman Filter
Amor Chowdhury, Andrej Sarjaš, 2016, original scientific article

Abstract: The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.
Keywords: accurate proximity measurement, sensor fusion algorithm, Unscented Kalman Filter, finite element modelling
Published in DKUM: 22.06.2017; Views: 1608; Downloads: 381
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