Title: Slope estimation of distributed electric drive mining dump truck based on multi-sensor integration

Authors: Yilin Wang; Weiwei Yang; Nong Zhang

Addresses: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, 230009, China

Abstract: The efficient, energy-saving, and unmanned operation of mines is an effective way to achieve safe production and improve efficiency. The current road slope estimation method has the shortcomings of involving more parameters and needing more accuracy. This paper proposes a road slope estimation method that integrates the slope estimation methods based on the vehicle longitudinal dynamics model, based on acceleration sensors, and based on inclination sensors with the help of the Kalman filtering algorithm based on covariance-weighted integration. Combined with the slope characteristics of the open pit mine, it obtains the estimation value with an accuracy of less than 8.4% compared to the actual road slope variation. The effectiveness and real-time performance of the multi-sensor integration method for the slope estimation of the distributed electric drive mining dump truck are verified, and the solution idea for the state parameter estimation related to the vehicle control strategy research is provided.

Keywords: distributed electric drive mining dump truck; state parameter estimation; slope estimation; multi-sensor integration; Kalman filter algorithm.

DOI: 10.1504/IJVD.2024.146774

International Journal of Vehicle Design, 2024 Vol.96 No.3/4, pp.263 - 285

Received: 05 Feb 2024
Accepted: 02 Dec 2024
Published online: 17 Jun 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article