Improving Nonlinear State Estimation Techniques by Hybrid Structures
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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 ...
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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