Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Abstract

Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

Unscented Kalman Filter-Based Robust State and Parameter Estimation for Free Radical Polymerization of Styrene with Variable Parameters

“…Generally, UKF performs two UTs, using the state equation and measurement equation for nonlinear transfer, which correspond to the time update phase and measurement update phase of UKF, respectively. For the standard nonlinear system, the steps of UKF are as follows [ 4 , 25 ].…”

“…Rodríguez et al proposed a dual KF, which is used for state estimation and parameter estimation [ 24 ]. Wan et al proposed a new UKF algorithm combined with the Huber method [ 25 ]. Lee and Song proposed a dual adaptive UKF to identify parameters of a dynamic system [ 26 ].…”

A Robust Hierarchical Estimation Scheme for Vehicle State Based on Maximum Correntropy Square-Root Cubature Kalman Filter

“…The Huber-based Kalman filter has also become an option for dealing with non-Gaussian noise. In the literature, vehicle states and parameters were estimated simultaneously using Huber-based robust UKF [ 23 ], but when Huber’s weight function error is large, the filtering accuracy of this method will be reduced due to inaccurate measurement information. Traditional Kalman filtering is based on the minimum mean square error (MMSE) criterion, which has poor filtering performance in non-Gaussian environments [ 24 ].…”

Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

“…The square root cubature Kalman filter (SCKF) is an improved algorithm based on the CKF that uses square root approximation recursion to effectively reduce the algorithm's computational complexity while ensuring accuracy, stability, and realtime performance [13,14]. To design the vehicle state observer, which is based on the Kalman filter algorithm, it is essential to have a more accurate mathematical model as well as statistical properties of the known noise; otherwise, the prediction results will have large errors and even divergence in severe cases [15]. Zhang Zhida et al, used a weighted adaptive sliding window algorithm to adaptively adjust the measurement and process noise matrices of the SCKF algorithm, which has more improved accuracy and robustness than the conventional method [16].…”