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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.

This paper develops an adaptive idle speed control strategy for a V2, 1000 cc four-stroke, water-cooled, port injection SI engine. In order to verify the proposed strategy, the non-dimensional engine model including charging and torque dynamics is established in Matlab/Simulink software based on previously experimental verification. The integration of dynamics above will be a multi-input-single-output (MISO) system, which inputs are throttle angle and spark advance angle, and the output is engine speed. The proposed adaptive controller is developed on the model-based structure. The system parameters are updated by recursive least square (RLS) method so the system is able to represent the actual operation. The updated system parameters adjust control gain by derivation of closed-loop gain and pole placement.

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.

For vehicles with intake manifold absolute pressure (MAP) sensor, the intake air mass is obtained using speed-density method. Since the analog MAP signal will contain high frequency noise with uncertain amplitude, the MAP value obtained in the engine management system using angle based sampling will result in MAP value variation even for engine steady-state operation. In order to properly obtain a MAP value under nonlinear time-varying characteristics, a MAP estimation method based on a closed-loop model is proposed. First, an adaptive two-input single-output intake manifold model is constructed. The Recursive Least Square technique is utilized to on-line identify the intake manifold model with throttle opening angle and engine speed as inputs. The identified intake manifold model is then employed to estimate the MAP using the Kalman Filter.

The on-board diagnostic (OBD) technologies for automobiles have been well-developed; however, it could not be carried out on motorcycles directly since the operation conditions are quite different between automobiles and motorcycles. In this research, we propose a misfire detection strategy for motorcycles based on the characteristics of crankshaft rotational dynamics. At first, experiments were done on a 125cc motorcycle to investigate the variation of instantaneous crankshaft rotational speed in power stroke while the misfire events are injected at different engine operation conditions. In order to generate misfire events for the engine, a misfire generator is established for providing specific misfire rates. If a misfire takes place at higher engine speed, the instantaneous rotational speed will decline continuously during power stroke due to higher friction losses, which leads to the reduction of average crankshaft rotational speed as well.

Range extender (RE), combined by an engine and a generator, charges the battery on the electric vehicle. Power management strategy of a range extended electric vehicle (REEV) will determine the required charging power according to battery state of charge (SOC) and driver demands. The charging power demand will be further converted into required operation torque and rotational speed demands from engine. Torque-based engine management system (EMS) is, therefore, required to receive the torque command from power management strategy for controlling the engine at required torque. This research develops a torque-based EMS for a RE engine which is a 125cc four-stroke semi-direct injection engine and fueled by liquefied petroleum gas (LPG). The RE engine is operated to provide stable power output for driving generator, so we only select two operating points for this engine. The first operating point is for higher power output and better fuel economy.