EKF-based adaptation of look-up tables with an air mass-flow sensor application

A method for bias compensation and online map adaptation using extended Kalman filters is developed. Key properties of the approach include the methods of handling component aging, varying measurement quality including operating-point-dependent reliability and occasional outliers, and operating-point-dependent model quality. Theoretical results about local and global observability, specifically adapted to the map adaptation problem, are proven. In addition, a method is presented to handle covariance growth of locally unobservable modes, which is inherent in the map adaptation problem. The approach is also applicable to the offline calibration of maps, in which case the only requirement of the data is that the entire operating region of the system is covered, i.e., no special calibration cycles are required. The approach is applied to a truck engine in which an air mass-flow sensor adaptation map is estimated during a European transient cycle. It is demonstrated that the method manages to find a map describing the sensor error in the presence of model errors on a measurement sequence not specifically designed for adaptation. It is also demonstrated that the method integrates well with traditional engineering tools, allowing prior knowledge about specific model errors to be incorporated and handled.

Research highlights
► A method for online map adaptation using the EKF as a parameter estimator is developed and analyzed.
► Key properties include handling of component aging, occasional spurious measurements, and operating point dependent model quality.
► Theoretical results about observability, specifically adapted to map adaptation are discussed and proven.