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

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

Nonlinear Recursive Estimation With Estimability Analysis for Physical and Semiphysical Engine Model Parameters

A methodology for nonlinear recursive parameter estimation with parameter estimability analysis for physical and semiphysical engine models is presented. Orthogonal estimability analysis based on parameter sensitivity is employed with the purpose of evaluating a rank of estimable parameters given multiple sets of observation data that were acquired from a transient engine testing facility. The qualitative information gained from the estimability analysis is then used for estimating the estimable parameters by using two well-known nonlinear adaptive estimation algorithms known as extended Kalman filter (EKF) and unscented Kalman filter (UKF). The findings of this work contribute on understanding the real-world challenges which are involved in the effective implementation of system identification techniques suitable for online nonlinear estimation of parameters with physical interpretation

Nonlinear recursive estimation with estimability analysis for physical and semiphysical engine model parameters

A methodology for nonlinear recursive parameter estimation with parameter estimability analysis for physical and semi-physical engine models is presented. Orthogonal estimability analysis based on parameter sensitivity is employed with the purpose of evaluating a rank of estimable parameters given multiple sets of observation data that were acquired from a transient engine testing facility. The qualitative information gained from the estimability analysis is then used for estimating the estimable parameters by using two well-known nonlinear adaptive estimation algorithms known as Extended and Unscented Kalman Filters. The findings of this work contribute on understanding the real-world challenges which are involved in the effective implementation of system identification techniques suitable for online nonlinear estimation of parameters with physical interpretation

Health monitoring system for transmission shafts based on adaptive parameter identification

A health monitoring system for a transmission shaft is proposed. The solution is based on the real-time identification of the physical characteristics of the transmission shaft i.e. stiffness and damping coefficients, by using a physical oriented model and linear recursive identification. The efficacy of the suggested condition monitoring system is demonstrated on a prototype transient engine testing facility equipped with a transmission shaft capable of varying its physical properties. Simulation studies reveal that coupling shaft faults can be detected and isolated using the proposed condition monitoring system. Besides, the performance of various recursive identification algorithms is addressed. The results of this work recommend that the health status of engine dynamometer shafts can be monitored using a simple lumped-parameter shaft model and a linear recursive identification algorithm which makes the concept practically viable

Dynamic modeling of a transient engine test cell for cold engine testing applications

NoThe increasing complexity in the development and manufacturing process of internal combustion engines leads to a higher demand for more effective testing and monitoring methods. Cold engine testing becomes progressively the main End-of-Line test which is used nowadays from automotive engine manufacturers with the purpose of determining the integrity of engine assembly. The present work is focused on the development of a detailed physics-based, lumped-parameter, dynamic model of a single cylinder internal combustion engine coupled with an alternating current transient dynamometer for cold engine testing applications. The overall transient engine test cell model is described based on a two-inertia system model consisting of the engine, the dynamometer and the coupling shaft. The internal combustion engine is modelled based on First Law of Thermodynamics and Second Newton’s Law for rotational bodies. The transient dynamometer is actually an alternating current three-phase induction motor which is modelled according to direct-quadrature axis approach, and its drive unit which is responsible for controlling the speed of the motor using indirect field orientation scheme. The engine and dynamometer are connected through a coupling shaft which is modelled as a compliant member with damping. The model is validated against experimental measurements such as engine cylinder pressure, engine excitation torque and alternating currents of the induction motor. All of the experimental measurements were recorded from an identical single cylinder transient engine test cell using a highly advanced instrumentation system. The described model serves as an ideal platform for developing innovative model-based fault detection and diagnosis techniques for cold engine testing applications. In conclusion, this is presented successfully for two simulated fault cases, a process fault and a sensor fault, proving the functionality and usefulness of the model