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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.

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.

This paper established a hardware-in-the-loop (HIL) system for developing the engine management system (EMS) of a motorcycle, which combines xPC target real-time simulator and PC based controller. An engine model of 125cc four-stroke gasoline engine that included the calculation of cylinder pressure is employed to be a control plant. A proposed control strategy developed with application of this HIL is verified to be superior to the conventional EMS of motorcycles. The proposed controller can send fuel injection and spark ignition control signals every two revolutions accurately via stroke identification method. During the engine running period, perhaps the ignition coil or other electronic equipment will conduct noise that interfere crankshaft tooth signal. Therefore, a Kalman filter is designed to improve the robustness of controller. Under interference, the performance of proposed controller is more satisfied than that without 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.