The Method of Mass Estimation Considering System Error in Vehicle Longitudinal Dynamics

Abstract

Vehicle mass is a critical parameter for economic cruise control. With the development of active control, vehicle mass estimation in real-time situations is becoming notably important. Normal state estimators regard system error as white noise, but many sources of error, such as the accuracy of measured parameters, environment and vehicle motion state, cause system error to become colored noise. This paper presents a mass estimation method that considers system error as colored noise. The system error is considered an unknown parameter that must be estimated. The recursive least squares algorithm with two unknown parameters is used to estimate both vehicle mass and system error. The system error of longitudinal dynamics is analyzed in both qualitative and quantitative aspects. The road tests indicate that the percentage of mass error is 16%, and, if the system error is considered, the percentage of mass error is 7.2%. The precision of mass estimation improves by 8.8%. The accuracy and stability of mass estimation obviously improves with the consideration of system error. The proposed model can offer online mass estimation for intelligent vehicle, especially for heavy-duty vehicle (HDV).

Intelligent Two-Step Estimation Approach for Vehicle Mass and Road Grade

“…where Fdrive, Facc, Faero, Froll, Fgrade are the vehicle's driving force, the inertia force, the aerodynamic drag force, the rolling resistance, the grade resistance, respectively; Ferr is the system error, which caused by uncertain environment disturbance in longitudinal dynamics [15]. The torque Ttq from the engine or driving motor is delivered to the driving wheels through the driveline, generating a circumferential force on the ground.…”

“…Fathy et al [14] proposed an online mass estimation approach based on recursive least square (RLS) algorithm and a fuzzy supervisor, which was used to extract parameters of high-frequency components when the vehicle has significant longitudinal motion. Lin et al [15] considered the system error as an unknown parameter, and it was estimated together with the vehicle mass using RLS. RLS with multiple forgetting factors (RLS-MFF) was adopted in simultaneous estimation of mass and grade to deal with the problem of different change rates for the two parameters [16].…”

Intelligent estimation for electric vehicle mass with unknown uncertainties based on particle filter

“…In this paper, vehicle mass estimated mainly aims to derive driving resistance or vehicle body equivalent moment of inertia for powertrain control based on vehicle longitudinal vehicle dynamic equation and consider rolling resistance coefficient, air drag coefficient is difficult to be known, therefore, resistance oscillations caused by these factors are regarded as high‐power noise and uncertainty and then vehicle mass is equivalent. However, RLS is vulnerable to noise or outliers and it will produce biased parameter estimation and may even make the estimator unsteady [10–13]. In order to solve non‐linear problems, KF‐based suboptimal approximation for non‐linear filtering algorithms using Gauss approximation is invented, such as Extended KF (EKF), Unscented KF (UKF).…”

A New Adaptive Mass Estimation Approach of Heavy Truck Based on Engine Torque Local Convex Minimum Characteristic at Low Speeds

“…Based on remotely collected video and audio data deducing a vehicle's position, engine torque-induced frame twist, and engine speed, the vehicle's mass-to-spring constant ratio can be calculated so that the vehicle mass can be obtained. Furthermore, reference [18] analyzed the system error of vehicle longitudinal dynamics in RLS estimation approach and addressed that, if the error is considered in an estimate, the estimation error can be reduced. However, most of the current mass estimation techniques require the parameters of longitudinal dynamics, such as aerodynamic parameters, rolling resistance coefficient, and so on, which are quite cumbersome to be measured accurately and possibly determined with some errors.…”