Dual extended Kalman filter for vehicle state and parameter estimation

TLDR

The state and parameter estimation problems are inherently interdependent and cannot be entirely separated. The article demonstrates a model‑based vehicle estimator for simultaneous estimation of vehicle states and parameters. The estimator uses a dual extended Kalman filter with two parallel Kalman filters, based on a four‑wheel, four‑degree‑of‑freedom vehicle model and interchangeable tyre models, and the paper demonstrates its appropriateness. The DEKF allows the parameter estimator to be switched off after sufficient accuracy is achieved, and results show it is an effective approach with potential benefit to the automotive industry.

Abstract

The article demonstrates the implementation of a model-based vehicle estimator, which can be used for combined estimation of vehicle states and parameters. The estimator is realised using the dual extended Kalman filter (DEKF) technique, which makes use of two Kalman filters running in parallel, thus 'splitting' the state and parameter estimation problems. Note that the two problems cannot be entirely separated due to their inherent interdependencies. This technique provides several advantages, such as the possibility to switch off the parameter estimator, once a sufficiently good set of estimates has been obtained. The estimator is based on a four-wheel vehicle model with four degrees of freedom, which accommodates the dominant modes only, and is designed to make use of several interchangeable tyre models. The paper demonstrates the appropriateness of the DEKF. Results to date indicate that this is an effective approach, which is considered to be of potential benefit to the automotive industry.