A comparative study on identification of vehicle inertial parameters

Abstract

This paper presents a comparative analysis of different analytical methods for identification of vehicle inertial parameters. The effectiveness of four different identification methods namely Recursive Least Squares (RLS), Recursive Kalman Filter (RKF), Gradient, and Extended Kalman Filter (EKF) for estimation of mass, moment of inertia and location of center of gravity of a vehicle is investigated. Requirements, capabilities and drawbacks of each method for real time applications are highlighted based on a comprehensive simulation analysis using CarSim. The Extended Kalman Filter method is shown to be the most reliable method for online identification of vehicle inertial parameters for active vehicle control, vehicle stability, and driver assistant systems.

Simultaneous Vehicle Real-Time Longitudinal and Lateral Velocity Estimation

“…This factor changes according to conditions of the road surface (icy, wet, dry, etc.). Because the contribution of in Equation (29) is small [32], it is assumed that 2 ≈ 0. Internal friction state is , and is the Stribeck relative velocity and is a parameter to capture the steady state friction/slip characteristic.…”

A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle׳ mass for a hybrid electric bus

“…Zarringhalam recursive Kalman filter (RKF), gradient and extended Kalman filter (EKF) to identify the vehicle inertial parameters. In their study, the EKF method was shown to be the most reliable method for online estimation of vehicle mass where mass value remains constant in the entire process [6,7]. Rajamani et al developed a realistic model of the suspension system to identify the sprung mass of automobile, which is determined by the number of passengers and the load on the vehicle [8].…”

“…First, the most parameter estimations could be obtained in the online form, but for road grade and vehicle mass estimation, EKF and RLS algorithms exhibit distinct advantages over other approaches. [6][7][8][9][10][11][12][13][14]. Second, the estimation of road grade could be obtained through different sensors, such as GPS installed on the vehicles [9,18,19].…”

A Novel Approach for Vehicle Inertial Parameter Identification Using a Dual Kalman Filter

“…Vehicle inertial parameter estimation algorithms are usually classified based on the parameters of interest and the vehicle dynamics model used. For instance, De Bruyne et al [1], Zarringhalam et al [2], and Rajamani and Hedrick [3] estimate the vehicle mass, the moment of inertia, and the CoG position by using the vehicle vertical dynamics. Bae et al [4], Winstead and Kolmanovsky [5], and Vahidi et al [6] utilize the vehicle longitudinal dynamics to estimate the vehicle mass and road grade.…”

“…Recursive Least Squares (RLS) and Kalman Filter (KF) have fast convergence, and can be easily implemented [2]. However, they cannot be used with nonlinear vehicle models [10].…”