四驱车辆多信息自适应融合纵向状态估计方法

doi:10.3901/JME.260277

机械工程学报 ›› 2026, Vol. 62 ›› Issue (8): 157-168.doi: 10.3901/JME.260277

• 特邀专辑：汽车线控底盘 • 上一篇下一篇

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四驱车辆多信息自适应融合纵向状态估计方法

周道林¹, 王翔宇¹, 屈新田², 万若里², 邵东¹, 李亮¹

1. 清华大学汽车安全与节能国家重点实验室北京 100084;
2. 东风汽车集团股份有限公司研发总院武汉 430058

收稿日期:2025-05-20 修回日期:2025-11-25 出版日期:2026-04-20 发布日期:2026-06-12
作者简介:周道林,男,1997年出生,博士研究生。主要研究方向为车辆动力学与控制。E-mail:zhoudl24@mails.tsinghua.edu.cn;王翔宇,男,1993年出生,博士。主要研究方向为混合动力技术、车辆线控技术。E-mail:wangxy_15@163.com
基金资助:
国家自然科学基金资助项目(52472412,52394262)。

Longitudinal State Estimation of 4WD Vehicles Using Multi Information Adaptive Fusion Algorithm

ZHOU Daolin¹, WANG Xiangyu¹, QU Xintian², WAN Ruoli², SHAO Dong¹, LI Liang¹

1. State Key Laboratory of Inter Safety and Energy, Tsinghua University, Beijing 100084;
2. DongFeng Motor Corporation Research&Development Institute, Wuhan 430058

Received:2025-05-20 Revised:2025-11-25 Online:2026-04-20 Published:2026-06-12

摘要/Abstract

摘要： 针对四驱车辆在复杂工况下纵向状态难以准确估计、影响牵引力控制系统(Traction control system,TCS)性能的问题，提出一种融合多传感器信息、轮胎非线性附着特性与轮速稳定性信息的自适应纵向状态估计算法(Multi-source information adaptive fusion algorithm,MIAFA)。该方法根据信号可信度，自适应融合基于动力学模型的纵向加速度与基于轮速信号的纵向速度估计结果，提高了纵向状态估计精度。在轮胎打滑工况下，考虑其非线性力学特性，基于纵向滑移率与侧偏角构建了非线性附着模型，并融合惯性测量单元与转向角等传感器数据，实现纵/侧向附着的统一建模。随后，利用卡尔曼滤波估计附着系数与轮胎力，并基于动力学模型实现车辆纵向加速度计算。为增强算法在复杂工况下的适应性，构建了基于轮胎滑移率与加速度的轮速稳定性相图，基于轮速传感器信息实现了车辆纵向速度计算。仿真与实车试验结果表明，所提方法能在低附着与复杂驱动-制动-转向联合工况下有效提升纵向状态估计精度，从而增强四驱车辆TCS的控制性能。

关键词: 四驱车辆, 驱动防滑控制, 纵向状态估计, 轮胎力估计, 多信息融合算法

Abstract: To address the challenge of inaccurate longitudinal state estimation in four-wheel-drive(4 WD) vehicles under complex driving conditions, which limits the performance of traction control systems(TCS), this paper proposes a multi-source Information adaptive fusion algorithm(MIAFA). The algorithm adaptively fuses vehicle dynamics-based acceleration and wheel-speed-based velocity estimates according to signal reliability. Considering tire nonlinearity under slip conditions, a unified tire-road adhesion model is developed using longitudinal slip and lateral slip angle, combined with multi-sensor data such as IMU and steering angle. Kalman filtering is applied to estimate tire forces and adhesion coefficients, while the vehicle dynamics model provides longitudinal acceleration. To further enhance robustness, a wheel speed stability map based on slip ratio and acceleration is used to refine longitudinal velocity estimation. Simulation and experimental results on low-adhesion roads demonstrate that the proposed MIAFA improves state estimation accuracy under complex combined driving scenarios, effectively enhancing TCS performance in 4 WD vehicles.

Key words: four-wheel-drive vehicles, traction control system, longitudinal state estimation, tire force estimation, multi-source information fusion algorithm

中图分类号:

U461

周道林, 王翔宇, 屈新田, 万若里, 邵东, 李亮. 四驱车辆多信息自适应融合纵向状态估计方法[J]. 机械工程学报, 2026, 62(8): 157-168.

ZHOU Daolin, WANG Xiangyu, QU Xintian, WAN Ruoli, SHAO Dong, LI Liang. Longitudinal State Estimation of 4WD Vehicles Using Multi Information Adaptive Fusion Algorithm[J]. Journal of Mechanical Engineering, 2026, 62(8): 157-168.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260277

http://www.cjmenet.com.cn/CN/Y2026/V62/I8/157

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四驱车辆多信息自适应融合纵向状态估计方法

Longitudinal State Estimation of 4WD Vehicles Using Multi Information Adaptive Fusion Algorithm

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