Estimation Algorithm for Vehicle Motion Parameters Based on Innovation Covariance in AC Chassis Dynamometer

When the alternating current (AC) chassis dynamometer system measures the motion parameters of a test vehicle, it is subject to interference from measurement noise, leading to an increase in testing errors. An innovative adaptive Kalman Filtering (KF) algorithm based on innovation covariance is proposed. This algorithm facilitates the optimal estimation of vehicle motion parameters without necessitating prior error statistics. The loading model of the measurement and control system is optimized, enabling the precise loading of the dynamometer. The test results indicate that the testing error of the optimized algorithm for the loading model decreases from 6.4% to 1.8%. This improvement establishes a foundation for achieving accurate control of the chassis dynamometer and minimizing testing errors.