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The paper presents the estimator design procedures for automotive power train systems based on the adaptive Kalman filter. The Kalman filter adaptation is based on a simple and robust algorithm that detects sudden changes of power train variables. The adaptive Kalman filter has been used to estimate the SI engine load torque and air mass flow, and also the tire traction force and road condition. The presented experimental results indicate that proposed estimators are characterized by favorable response speeds and good noise suppression abilities.

This paper presents a detailed experimental analysis of potentials for road condition estimation based on the tire static curve gradient in the low-slip region, and the damping of the 40 Hz tire vibration mode. An experimental vehicle equipped with four standard ABS sensors (44 pulses/rev), two special high-resolution wheel speed sensors (512 pulses/rev), and a halfshaft torque sensor was utilized for collecting the experimental data. The test data were recorded on a test track characterized by different road conditions such as dry concrete, wet and dry ice, and wet and dry snow.

The paper presents a detailed design procedure of an SI engine load torque estimator based on an adaptive Kalman filter. The adaptation mechanism is based on a simple and robust load torque change detection algorithm. The presented simulation and experimental results point out that the estimator is characterized by a fast response and good noise suppression. The proposed estimator is used to establish a fast load torque compensation path in an idle speed control system. The presented simulation and experimental results show that this yields a significant improvement of idle speed control performance