Qi Zhao's research while affiliated with Technische Universität Clausthal and other places
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Publications (4)
The online estimation of fuel property parameters (density and bulk modulus) for use in automotive engine control is considered. The estimation is carried out through state augmentation (including the parameters in the state vector) and use of an Unscented Kalman Filter (UKF) that is based on a physical model of the common rail fuel injection syste...
A novel method for combined state and parameter estimation is presented. The method is based on the recursive prediction error method for nonlinear system and is able to estimate states and parameters from nonlinear state-space models simultaneously. Moreover, because of the application of the deterministic sampling approach, no model linearization...
Accurate state-of-charge information is of great importance for battery management system. In this paper, a novel electrical equivalent model for polymer Li-Ion battery is developed and Bayesian filtering methods are employed to estimate the SOC of Lithium-ion Polymer battery accurately.
Citations
... However, the position of the clutch touch point will change and with wear will tend to the depressed clutch position. For these reasons, adapting the clutch touch point can be just as important as adapting the coefficient of friction of the clutch [7,8]. ...
... Diagnostics of CR system and especially the diagnostics of CR injectors have been widely studied e.g. [2][3][4][5][6][7][8][9]. Krogerus et al. [10] have presented a survey of the analysis, modelling, and diagnostics of diesel fuel injection systems. ...
... This requires sensitivity models, i.e. the state estimation derivative and the error covariance matrix derivative w.r.t. the parameter vector. Analogously, an adaptive UKF combined with a RPE method for parameter and covariance estimation is presented in [11], but the proposed approach uses an EKF weighting factor for the RPE method. In [12] an adaptive UKF has been proposed which uses also sensitivity models, but in this case process noise covariance values are only estimated while a cost functional based on a time-averaged approximation of innovation covariance is minimized. ...
