Beschreibung
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.
Autorenporträt
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.
Herstellerkennzeichnung:
OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
E-Mail: info@omniscriptum.com
