ArticleSensors (Basel, Switzerland)2021
Estimation of Vehicle Dynamic Parameters Based on the Two-Stage Estimation Method.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System.Sensors (Basel, Switzerland) · 2023Article
- Advanced Sensing and Control for Connected and Automated Vehicles.Sensors (Basel, Switzerland) · 2022Article
- A Take-Over Performance Evaluation Model for Automated Vehicles from Automated to Manual Driving.Computational intelligence and neuroscience · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Vehicle dynamic parameters are of vital importance to establish feasible vehicle models which are used to provide active controls and automated driving control. However, most vehicle dynamics parameters are difficult to obtain directly. In this paper, a new method, which requires only conventional sensors, is proposed to estimate vehicle dynamic parameters. The influence of vehicle dynamic parameters on vehicle dynamics often involves coupling. To solve the problem of coupling, a two-stage estimation method, consisting of multiple-models and the Unscented Kalman Filter, is proposed in this paper. During the first stage, the longitudinal vehicle dynamics model is used. Through vehicle acceleration/deceleration, this model can be used to estimate the distance between the vehicle centroid and vehicle front, the height of vehicle centroid and tire longitudinal stiffness. The estimated parameter can be used in the second stage. During the second stage, a single-track with roll dynamics vehicle model is adopted. By making vehicle continuous steering, this vehicle model can be used to estimate tire cornering stiffness, the vehicle moment of inertia around the yaw axis and the moment of inertia around the longitudinal axis. The simulation results show that the proposed method is effective and vehicle dynamic parameters can be well estimated.
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Registered trials
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