In this work, a nonlinear hybrid state space model of a complete spark ignition (SI) gasoline engine system from throttle to muffler is developed using the mass and energy balance equations. It provides within-cycle dynamics of all the engine variables such as temperature, pressure, and mass of individual gas species in the intake manifold (IM), cylinder, and exhaust manifold (EM). The inputs to the model are the same as that commonly exercised by the engine control unit (ECU), and its outputs correspond to available engine sensors. It uses generally known engine parameters, does not require extensive engine maps found in mean value models (MVMs), and requires minimal experimentation for tuning. It is demonstrated that the model is able to capture a variety of engine faults by suitable parameterization. The state space modeling is parsimonious in having the minimum number of integrators in the model by appropriate choice of state. It leads to great computational efficiency due to the possibility of deriving the Jacobian expressions analytically in applications such as on-board state estimation. The model was validated both with data from an industry standard engine simulation and those from an actual engine after relevant modifications. For the test engine, the engine speed and crank angle were extracted from the crank position sensor signal. The model was seen to match the true values of engine variables both in simulation and experiments.
In this work, an Extended Kalman Filter (EKF) based tunable diagnoser, which uses a minimal hybrid nonlinear state space model of a spark ignition (SI) four stroke engine, is used for the detection and isolation of a variety of engine system faults including intake manifold leak, injector fault and exhaust manifold leak. The state estimates and innovation sequences from the EKF based estimator are shown to be adequate for the detection and isolation of the faults under consideration. Once a fault is detected and isolated, the diagnoser could be tuned online to perform fault identification by redefining a model/fault parameter as an additional state to be estimated and then performing a joint state and parameter estimation. The engine model and diagnoser are implemented in SimulinkTM and are validated against an AMESimTM model of the engine. For the nominal engine model, the performance of the EKF estimator is compared with two other computationally more expensive nonlinear estimators, namely the Unscented Kalman Filter (UKF) and Rao-Blackwell Particle Filter (RBPF).
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