This research investigates the way in which a nonlinear Gaussian Unscented Kalman Filter can be combined with a nonlinear Bayesian Particles Filter in a hybrid structure capable to perform better in comparison to each one of these two estimators. Their state estimation performance is evaluated for the same case study, more precisely for a simplified model of a Ni-MH battery that is integrated in a Battery Management System in order to drive a Hybrid Electric Vehicle. A benchmark evaluates the performance of each estimator in terms of root mean square error, mean square error and mean absolute error statistics based on the battery state of charge residual. From this benchmark it is easy to get information about the battery state of charge estimation accuracy, the robustness and the modelling limitations of both estimators. The extensive simulations in real time are carried out in an attractive real-time MATLAB framework environment.
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BS (Maths, 1982), PhD (Automation, 1990, Romania, Electrical Eng., Concordia University, Montreal, Canada, 2001). 1979-1993 Associate Prof. at Automation and Control Department - University of Craiova. 2001 - Prof at John Abbott College, Montreal, Canada. Interests: Automation and control, Estimation, FDI, Fuzzy Logic, Neural network.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This research investigates the way in which a nonlinear Gaussian Unscented Kalman Filter can be combined with a nonlinear Bayesian Particles Filter in a hybrid structure capable to perform better in comparison to each one of these two estimators. Their state estimation performance is evaluated for the same case study, more precisely for a simplified model of a Ni-MH battery that is integrated in a Battery Management System in order to drive a Hybrid Electric Vehicle. A benchmark evaluates the performance of each estimator in terms of root mean square error, mean square error and mean absolute error statistics based on the battery state of charge residual. From this benchmark it is easy to get information about the battery state of charge estimation accuracy, the robustness and the modelling limitations of both estimators. The extensive simulations in real time are carried out in an attractive real-time MATLAB framework environment. 60 pp. Englisch. Bestandsnummer des Verkäufers 9783330044180
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tudoroiu NicolaeBS (Maths, 1982), PhD (Automation, 1990, Romania, Electrical Eng., Concordia University, Montreal, Canada, 2001). 1979-1993 Associate Prof. at Automation and Control Department - University of Craiova. 2001 - Prof at . Bestandsnummer des Verkäufers 158958540
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This research investigates the way in which a nonlinear Gaussian Unscented Kalman Filter can be combined with a nonlinear Bayesian Particles Filter in a hybrid structure capable to perform better in comparison to each one of these two estimators. Their state estimation performance is evaluated for the same case study, more precisely for a simplified model of a Ni-MH battery that is integrated in a Battery Management System in order to drive a Hybrid Electric Vehicle. A benchmark evaluates the performance of each estimator in terms of root mean square error, mean square error and mean absolute error statistics based on the battery state of charge residual. From this benchmark it is easy to get information about the battery state of charge estimation accuracy, the robustness and the modelling limitations of both estimators. The extensive simulations in real time are carried out in an attractive real-time MATLAB framework environment.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch. Bestandsnummer des Verkäufers 9783330044180
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This research investigates the way in which a nonlinear Gaussian Unscented Kalman Filter can be combined with a nonlinear Bayesian Particles Filter in a hybrid structure capable to perform better in comparison to each one of these two estimators. Their state estimation performance is evaluated for the same case study, more precisely for a simplified model of a Ni-MH battery that is integrated in a Battery Management System in order to drive a Hybrid Electric Vehicle. A benchmark evaluates the performance of each estimator in terms of root mean square error, mean square error and mean absolute error statistics based on the battery state of charge residual. From this benchmark it is easy to get information about the battery state of charge estimation accuracy, the robustness and the modelling limitations of both estimators. The extensive simulations in real time are carried out in an attractive real-time MATLAB framework environment. Bestandsnummer des Verkäufers 9783330044180
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Taschenbuch. Zustand: Neu. Improving Nonlinear State Estimation Techniques by Hybrid Structures | Nicolae Tudoroiu (u. a.) | Taschenbuch | 60 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330044180 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 108391663
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