Estimation of Engine Rotational Dynamics Using Kalman Filter Based on a Kinematic Model

For a conventional scooter engine with a four-plus-one-tooth crankshaft wheel, not only is the crankshaft position estimation insufficient due to poor angle resolution, but the speed measurement might also be easily contaminated by the sensor noise. We proposed a Kalman filter with stroke identification to estimate the engine rotational dynamics. The design of the Kalman filter is based on a kinematic model that requires no engine parameters. A nonlinear engine model is used to evaluate the estimation performance of the conventional algorithm using a low-pass filter and the proposed algorithm at various operating conditions. Preliminary simulation and experimental results show that the proposed algorithm can mitigate the noise impact and result in estimations closer to the actual engine responses.