Use this URL to cite or link to this record in EThOS:
Title: Qualitative adaptive identification for powertrain systems : powertrain dynamic modelling and adaptive identification algorithms with identifiability analysis for real-time monitoring and detectability assessment of physical and semi-physical system parameters
Author: Souflas, Ioannis
ISNI:       0000 0004 6495 548X
Awarding Body: University of Bradford
Current Institution: University of Bradford
Date of Award: 2015
Availability of Full Text:
Access from EThOS:
Access from Institution:
A complete chain of analysis and synthesis system identification tools for detectability assessment and adaptive identification of parameters with physical interpretation that can be found commonly in control-oriented powertrain models is presented. This research is motivated from the fact that future powertrain control and monitoring systems will depend increasingly on physically oriented system models to reduce the complexity of existing control strategies and open the road to new environmentally friendly technologies. At the outset of this study a physics-based control-oriented dynamic model of a complete transient engine testing facility, consisting of a single cylinder engine, an alternating current dynamometer and a coupling shaft unit, is developed to investigate the functional relationships of the inputs, outputs and parameters of the system. Having understood these, algorithms for identifiability analysis and adaptive identification of parameters with physical interpretation are proposed. The efficacy of the recommended algorithms is illustrated with three novel practical applications. These are, the development of an on-line health monitoring system for engine dynamometer coupling shafts based on recursive estimation of shaft’s physical parameters, the sensitivity analysis and adaptive identification of engine friction parameters, and the non-linear recursive parameter estimation with parameter estimability analysis of physical and semi-physical cyclic engine torque model parameters. The findings of this research suggest that the combination of physics-based control oriented models with adaptive identification algorithms can lead to the development of component-based diagnosis and control strategies. Ultimately, this work contributes in the area of on-line fault diagnosis, fault tolerant and adaptive control for vehicular systems.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Automotive powertrains ; Dynamic modelling ; Physics-based ; Modelling ; Linear ; Nonlinear ; Identifiability ; Sensitivity ; Adaptive identification ; Recursive filters ; Condition monitoring ; Adaptive identification algorithms