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A model-based sensor fault detection algorithm is proposed in this paper to detect and isolate the faulty sensor. Wheel speeds are validated using the wheel speed deviations before being employed to check the sensor measurements of the vehicle dynamics. Kinematic models are employed to estimate yaw rate, lateral acceleration, and steering wheel angle. A Kalman filter based on a point mass model is employed to estimate longitudinal speed and acceleration. The estimated vehicle dynamics and sensor measurements are used to calculate the residuals. Adaptive threshold values are employed to identify the abnormal increments of residuals. Recursive least square method is used to design the coefficients of the expressions for adaptive threshold values, such that the false alarms caused by model uncertainties can be prevented. Different combinations of estimations are employed to obtain 18 residuals.

Fuel film dynamics in the intake manifold are considered to develop air fuel ratio (AFR) control strategy with on-line system identification for a V2 engine in this paper. A1000 cc four-stroke two-cylinder, water-cooled port injection SI engine is used as the target engine to develop the engine model in Matlab/Simulink. The model which consists of charging, fueling, combustion, friction, and engine rotational dynamics is used to verify the proposed AFR control. Since the fuel film dynamics changes with different engine operating conditions, the fuel film parameters are often listed as look-up tables for fuel film dynamics calculation in the conventional AFR control. However, those parameters might be inaccurate during transient engine operation. Different intake port temperature will affect the accuracy of those fuel film parameters as well. In order to solve this problem, recursive least square (RLS) is used to identify those parameters on-line.

This paper develops an engine model for the model-in-the-loop (MIL) and hardware-in-the-loop (HIL) application to shorten the time duration and reduce the costs of developing and verifying the engine management system (EMS). The target engine is a 1.0L V-type two cylinder water-cooled spark-ignition engine. The engine model is developed using a so-called modulization method, which includes to: (1) separate the sub-models according to the different physical phenomena; (2) collect the sub-models to establish a library; (3) execute the component modules based on a pre-determined sequence by a more flexible way. The engine model is then applied in MIL structure for testing and verifying the control strategies in the developed EMS. After all strategies are verified, the HIL structure is constructed by a hardware controller and a virtual engine in the xPC target. The execution time-step of engine model is analyzed to keep enough accuracy and numeric stability for real-time simulation.

Engine control units manage various conditions in an operating engine, including fuel injection, spark ignition and valve timing, in order to achieve the goals of high performance, high fuel efficiency and low emissions. Typically, engine models are necessary for developing engine control systems. Most mean value engine models (MVEM) are based on empirical volumetric efficiency, which contributes to calculating intake air flow rate. Therefore, they are not capable of simulating changes in valve lift and valve timing, and cannot be used for a variable valve train (VVT) engine. A method of calculating intake air flow rate with variable valve lift and valve timing is needed to adapt to the demands on VVT engine models. An engine model is proposed that focuses on a charging model, developed by using a filling-and-emptying model to simulate the air exchange in an engine, including intake- and exhaust-air flows.