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An optimized power management strategy for Belt-Starter Generator (BSG) mild hybrid system is proposed and used to study its benefit of fuel economy on a 2.2L turbo engine. First, a cost function is defined as fuel and battery power consumptions. In order to obtain an optimal fuel economy of BSG, Dynamic Programming (DP) optimization approach is employed to minimize the defined cost function over New European Driving Cycle (NEDC). A nonlinear vehicle dynamics simulation model is established using Matlab®/Simulink. Experimental data are utilized to verify the nonlinear model. Since the detailed simulation model is not suitable for the DP due to its large number of states, a simplified dynamic model is developed in this paper. An optimized power management strategy is extracted by analyzing the results of DP and is evaluated using NEDC. Preliminary simulation results show that the proposed strategy presents lower fuel consumption than that of the conventional strategy by about 11.4%.

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.

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.

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.

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.

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.

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.