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A data-driven method for the estimation of truck-state parameters and braking force distribution

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journal contribution
posted on 2023-02-14, 13:43 authored by Qunyi Chu, Wen Sun, Yuanjian ZhangYuanjian Zhang
In the study of braking force distribution of trucks, the accurate estimation of the state parameters of the vehicle is very critical. However, during the braking process, the state parameters of the vehicle present a highly nonlinear relationship that is difficult to estimate accurately and that seriously affects the accuracy of the braking force distribution strategy. To solve this problem, this paper proposes a machine-learning-based state-parameter estimation method to provide a solid data base for the braking force distribution strategy of the vehicle. Firstly, the actual collected complete vehicle information is processed for data; secondly, random forest is applied for the feature screening of data to reduce the data dimensionality; subsequently, the generalized regression neural network (GRNN) model is trained offline, and the vehicle state parameters are estimated online; the estimated parameters are used to implement the four-wheel braking force distribution strategy; finally, the effectiveness of the method is verified by joint simulation using MATLAB/Simulink and TruckSim.

Funding

State Scholarship Funding of CSC (202008320074)

Industry-University-Research Cooperation Project of Jiangsu Province (BY2021268)

Science and Technology Project of Changzhou (CZ20210033)

Natural Science Foundation of the Jiangsu Higher Education Institutions of China (22KJB580001)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Sensors

Volume

22

Issue

21

Publisher

MDPI

Version

  • VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This article is an Open Access article published by MDPI and distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Acceptance date

2022-10-25

Publication date

2022-10-31

Copyright date

2022

eISSN

1424-8220

Language

  • en

Depositor

Dr Yuanjian Zhang. Deposit date: 13 February 2023

Article number

8358