A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States

• Main Authors: Zheshuo Zhang, Jie Zhang, Jiawen Dai, Bangji Zhang, Hengmin Qi
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Sensors
• Subjects: vehicle dynamics; real-time parameter estimation; dual unscented Kalman filter; modal analysis; vehicle parameter identification
• Online Access: https://www.mdpi.com/1424-8220/21/12/4068

Vehicle parameters are essential for dynamic analysis and control systems. One problem of the current estimation algorithm for vehicles’ parameters is that: real-time estimation methods only identify parts of vehicle parameters, whereas other parameters such as suspension damping coefficients and suspension and tire stiffnesses are assumed to be known in advance by means of an inertial parameter measurement device (IPMD). In this study, a fusion algorithm is proposed for identifying comprehensive vehicle parameters without the help of an IPMD, and vehicle parameters are divided into time-independent parameters (TIPs) and time-dependent parameters (TDPs) based on whether they change over time. TIPs are identified by a hybrid-mass state-variable (HMSV). A dual unscented Kalman filter (DUKF) is applied to update both TDPs and online states. The experiment is conducted on a real two-axle vehicle and the test data are used to estimate both TIPs and TDPs to validate the accuracy of the proposed algorithm. Numerical simulations are performed to further investigate the algorithm’s performance in terms of sprung mass variation, model error because of linearization and various road conditions. The results from both the experiment and simulation show that the proposed algorithm can estimate TIPs as well as update TDPs and online states with high accuracy and quick convergence, and no requirement of road information.