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The phenomenon of fuel-film dynamics for four-stroke small-scale spark-ignition engines is investigated in this paper. A first-order fuel-film model, so-called tau-x model, is used to represent the fuel dynamics. The parameters of fuel-film model, which consists of the portion of fuel that deposited on the manifold wall and the time constant of the fuel evaporation process, are identified using the recursive least squared technique. Performances of the proposed algorithm are evaluated using a nonlinear engine model in Matlab/Simulink. The preliminary simulation results show that the proposed algorithm can accurately be used to identify the parameters of fuel-film model. The experimental data are then utilized to study the characteristics of fuel-film dynamics, and show that the fuel-film dynamics is significantly affected by engine speed, throttle opening, injection timing, and intake temperature.

Due to the nonlinear time-varying nature of the spark-ignition engine, an adaptive multi-input single-output (MISO) controller based on self-tuning regulator (STR) is proposed for idle speed control in this paper. The spark timing and idle air control are simultaneously employed as control inputs for maintaining the desired idle speed, and are designed based on P and PI type STR, respectively. The Recursive Least Square technique is employed to identify the engine as a first-order MISO linear model. Pole placement technique is then used to design the adaptive MISO controller. Performances of the proposed algorithm are evaluated using a nonlinear engine model in Matlab/Simulink. The system parameters with 10% uncertainties are also utilized to perform the associated robustness analysis. Preliminary simulation results show significant reduction of speed deviations under the presence of torque disturbances and model uncertainties.

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 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.

In order to study the noise effect of the crank angle sensor on electronic fuel injection (EFI) control system, a Kalman filter with stroke identification is employed to estimate the crankshaft rotational dynamics. Estimated crank angle and speed are then used for EFI system. A 125 c.c. scooter engine verified by the experimental data is used to design the Kalman filter. A simulation model, which consists of nonlinear engine dynamics, powertrain dynamics, tire dynamics, and pitch-plane motorcycle dynamics, is established in Matlab/Simulink to evaluate the performance of the Kalman filter at various noise conditions.

For a conventional scooter engine, not only the crankshaft position estimation is insufficient based on the one-tooth crankshaft wheel, but also the speed measurement might be contaminated by sensor noise easily. The authors propose a technique using Kalman filter to estimate crankshaft position and engine speed for digital ignition control of a scooter engine. A 125cc engine model, which is verified by the experimental data of the target engine, will be used to design the Kalman filter. A simulation model, which consists of nonlinear engine dynamics, powertrain dynamics, tire dynamics, and pitch plane dynamics, is used to evaluate the performance of proposed estimation algorithm with different tooth numbers of crankshaft wheel and various noise conditions.

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.

In order to reduce engine control strategy development time and cost, the Hardware-In-the-Loop (HIL) simulation technology is developed. This paper establishes a simulation model and control platform based on the HIL structure for studying control strategy development and verification. A 125c.c. engine model verified by the experimental data is established in Matlab/Simulink, which is used as a virtual engine and then implemented in the xPC real-time system. The Motorola MC 68376 controller chip provides control signals included injection duration/timing and ignition timing for the virtual engine. A PCI-6024E input/output board is used as an interface between the controller and the virtual engine. A simulation model, which consists of engine, powertrain, tire, and pitch plane dynamics, is used to evaluate the response of engine dynamics and longitudinal dynamics via HIL simulation.

A heat transfer model for small-scale spark-ignition engines has been proposed by authors in previous study. However, this model is only based on single engine, it may not be suitable for the others. In order to improve the accuracy of predicted heat transfer rate for different small-scale engines, another heat transfer model using Stanton number based on two engines is proposed. Prediction results of instantaneous heat flux and global heat transfer based on the proposed model are compared with the experimental results and prediction results of previous model. It is found that the proposed model has prediction results closer to the measured data than the previous models.

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.