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

doi:10.3901/JME.260277

Abstract

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

CLC Number:

U461

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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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260277

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

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