Mining truck sideslip angle estimation based on multiple methods weighted fusion

doi:10.3785/j.issn.1008-973X.2025.10.011

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (10): 2106-2114 DOI: 10.3785/j.issn.1008-973X.2025.10.011

Mining truck sideslip angle estimation based on multiple methods weighted fusion

Zhongxing LI(

),Yingzhu JIA,Guoqing GENG,Yixu QIN,Xinchang YANG

School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China

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Abstract

To address the challenge of estimating the sideslip angle of the mining truck in rugged terrain conditions, a sideslip angle estimation method based on the weighted fusion of the extended Kalman filter (EKF) method and the integration method was proposed. To accurately describe the vehicle’s motion state, a 17-DOF dynamic model of a mining truck incorporating both solid axle suspension and tandem suspension was established. Using a wheel-speed-based vehicle longitudinal speed estimator to obtain a preliminary estimate of the vehicle’s longitudinal velocity, a vehicle longitudinal and lateral velocity estimator based on EKF and a vehicle lateral velocity integration estimator were developed. Based on the characteristics of the EKF method and the integration method, a proportional-derivative fusion weight calculation method was proposed to fuse the two methods. Simulation results show that the proposed method can achieve accurate estimation of the vehicle’s sideslip angle by leveraging the advantages of both the EKF method and integration method, and has a good adaptability to the rugged terrain conditions.

Key words： mining truck state estimation fusion estimation dynamics model sideslip angle extended Kalman filter (EKF)

Received: 25 September 2024 Published: 27 October 2025

CLC:

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	Zhongxing LI
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	Xinchang YANG

Cite this article:

Zhongxing LI,Yingzhu JIA,Guoqing GENG,Yixu QIN,Xinchang YANG. Mining truck sideslip angle estimation based on multiple methods weighted fusion. Journal of ZheJiang University (Engineering Science), 2025, 59(10): 2106-2114.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.10.011 OR https://www.zjujournals.com/eng/Y2025/V59/I10/2106

基于多方法加权融合的矿用车质心侧偏角估计

针对矿用车在崎岖路面工况下质心侧偏角估计困难的问题，提出基于扩展卡尔曼滤波(EKF)方法和积分方法加权融合的质心侧偏角估计方法. 为了准确描述车辆运动状态，建立包含非独立悬架和平衡悬架的矿用车十七自由度动力学模型. 利用基于轮速的车辆纵向速度估计器获取车辆纵向速度初步估计值，构建基于EKF的车辆纵、横向速度估计器和车辆横向速度积分估计器. 根据EKF方法和积分方法的特点，提出比例-微分融合权重系数计算方法，借此对2种方法进行加权融合. 仿真实验结果表明，所提方法能够结合EKF方法和积分方法的优点，实现车辆质心侧偏角的准确估计，具有较好的崎岖路面工况适应能力.

关键词： 矿用车, 状态估计, 融合估计, 动力学模型, 质心侧偏角, 扩展卡尔曼滤波(EKF)

Tab.1 Parameters of mining truck

Fig.1 Seventeen-DOF dynamics model of mining truck

Fig.2 Longitudinal and normal tire forces in rugged terrain

Fig.3 Forces on left balance beam

Tab.2 Lateral force characteristic parameters of tires

Fig.4 Comparison of lateral velocity estimation effect between extended Kalman filter method and integral method

Fig.5 Proportional-derivative fusion weight calculation method

Fig.6 Flowchart of mining truck sideslip angle estimation method

Tab.3 Vehicle sideslip angle estimation error on twist road

Fig.7 Vehicle sideslip angle estimation result on twist road (H_s=150 mm)

Tab.4 Sideslip angle estimation error on single-sided bumps

Fig.8 Vehicle sideslip angle estimation result on single-sided bumps (H_s=180 mm)


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