scholarly journals On the Dynamic Mass-Estimation Algorithm of Displacement and Velocity Sensing Type

1983 ◽  
Vol 19 (4) ◽  
pp. 300-307 ◽  
Author(s):  
Toshiro ONO ◽  
Haruo SHIMAOKA
Keyword(s):  
Estimation Algorithm ◽  
Mass Estimation ◽  
Dynamic Mass

Truck mass estimation method based on the on-board sensor

2020 ◽  
Vol 234 (10-11) ◽  
pp. 2429-2443
Author(s):  
Beijia Wang ◽  
Hongliang Wang ◽  
Lei Wu ◽  
Liuliu Cai ◽  
Dawei Pi ◽  
...  
Keyword(s):  
Error Correction ◽  
Estimation Error ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Correction Method ◽  
Mass Estimation ◽  
The Road ◽  
Vehicle Mass ◽  
Error Correction Method ◽  
Road Gradient

Vehicle mass estimation is the key technology to improve vehicle stability. However, the existing mass estimation accuracy is easily affected by the change of road gradient, and there are few studies on the mass estimation method of the light truck. Aiming at this problem, this paper uses sensors to measure road gradient and rear suspension deformation and proposes a sensor-based vehicle mass estimation algorithm. First, factors that affect the mass estimation are analyzed, road gradient error correction method and mass estimation error correction method are established. Besides, the suspension deformation is decoupled from the road gradient. Second, the mass estimation algorithm model was established in Matlab/Simulink platform and compared with the mass estimation iterative algorithm. Finally, the road test was carried out under various conditions, the results show that the proposed mass estimation algorithm is robust, and the accuracy of the mass estimation will not be affected by the sudden change of road gradient.

A Fast Center of Mass Estimation Algorithm for Coordinates of IR Markers

2008 ◽  
Author(s):  
Lingfei Zhang ◽  
Gang Chen ◽  
Dong Ye ◽  
Rensheng Che
Keyword(s):  
Center Of Mass ◽  
Estimation Algorithm ◽  
Mass Estimation

scholarly journals Soot Mass Estimation From Electrical Capacitance Tomography Imaging for a Diesel Particulate Filter

2019 ◽  
Author(s):  
Salah Hassan ◽  
Sohel Anwar
Keyword(s):  
Cross Section ◽  
Estimation Algorithm ◽  
Particulate Filter ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Diesel Particulate ◽  
Mass Estimation ◽  
Soot Mass ◽  
The Cross ◽  
Capacitance Tomography

Abstract The Electrical capacitance tomography (ECT) method has recently been adapted to obtain tomographic images of the cross section of a diesel particulate filter (DPF). However, a soot mass estimation algorithm is still needed to translate the ECT image pixel data to obtain soot load in the DPF. In this paper, we propose an estimation method to quantify the soot load in a DPF through an inverse algorithm that uses the ECT images commonly generated by a back-projection algorithm. The grayscale pixel data generated from ECT is used in a matrix equation to estimate the permittivity distribution of the cross section of the DPF. Since these permittivity data has direct correlation with the soot mass present inside the DPF, a permittivity to soot mass distribution relationship is established first. A numerical estimation algorithm is then developed to compute the soot mass accounting for the mass distribution across the cross-section of the DPF as well as the dimension of the DPF along the exhaust flow direction. Experimental data has been used to validate the proposed soot estimation algorithm which compared the estimated values with the actual measured soot mass. The estimated soot mass for various soot load amounts were found to correlate reasonably well with the measured soot masses in those cases.

scholarly journals Enhancement of Impact Mass Estimation Algorithm for a Plate Type Structure

2007 ◽  
Vol 48 (6) ◽  
pp. 1249-1253
Author(s):  
Doo-Byung Yoon ◽  
Jin-Ho Park ◽  
Young-Chul Choi ◽  
Hyu-Sang Kwon ◽  
Joon-Hyun Lee
Keyword(s):  
Estimation Algorithm ◽  
Type Structure ◽  
Mass Estimation ◽  
Plate Type ◽  
Impact Mass

Wing mass estimation algorithm for medium range box wing aircraft

2013 ◽  
Vol 117 (1189) ◽  
pp. 329-340 ◽  
Author(s):  
P. O. Jemitola ◽  
G. Monterzino ◽  
J. Fielding
Keyword(s):  
Finite Element ◽  
Regression Analysis ◽  
Finite Element Methods ◽  
Fore Wing ◽  
Vortex Lattice ◽  
Estimation Algorithm ◽  
Correction Coefficient ◽  
Mass Estimation ◽  
Aircraft Wings ◽  
Medium Range

Abstract A procedure for defining an empirical formula for the mass estimation of the fore and aft wings field Uof a medium range box wing aircraft is described. The procedure is based upon the work of Howe for estimating the wing mass of conventional cantilever wing aircraft. The paper outlines the procedure used to relate conventional cantilever wings to box wing aircraft wings. Using a vortex lattice tool, finite element methods and regression analysis, the modification performed on the coefficient in Howe’s method to enable its use on a medium range box wing aircraft is outlined. The results show that the fore and aft wings would use the same correction coefficient and that the aft wing would therefore be lighter than the fore wing on a medium range box wing aircraft.

Two-layer structure algorithm for estimation of commercial vehicle mass

2019 ◽  
Vol 234 (2-3) ◽  
pp. 378-389
Author(s):  
Liuliu Cai ◽  
Hongliang Wang ◽  
Tianle Jia ◽  
Pai Peng ◽  
Dawei Pi ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Extended Kalman Filter ◽  
Commercial Vehicle ◽  
Layer Structure ◽  
Estimation Algorithm ◽  
Mean Square ◽  
Mass Estimation ◽  
Grade Estimation

Aiming at the problem of mass estimation for commercial vehicle, a two-layer structure mass estimation algorithm was proposed. The first layer was the grade estimation algorithm based on recursive least squares method and the second layer was a mass estimation algorithm using the extended Kalman filter. The estimated grade was introduced as the observation quantity of the second layer. The influence of the suspension deformation on grade estimation was considered in the first layer algorithm, which was corrected in real time according to the mass and road grade estimated by the second layer algorithm. The proposed estimation algorithm was validated via a co-simulation platform involving TruckSim and MATLAB/Simulink. Finally, a road test was carried out, and the evaluation method using the root mean square error was proposed. According to the test, the average value of the root mean square error reduces from 871.65 to 772.52, grade estimation is more accurate, and the convergence speed of mass estimation is faster, compared with estimation results of the extended Kalman filter method.

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