Online Estimation of Tire Normal Force with Applying Hardware-Software Couple Model

Document Type : Research Article

Authors

1 Department of Mechanical Engineering, Shahid Bahonar University of Kerman

2 Department of Mechanical Engineering, Shahid Bahonar university of Kerman

Abstract

Tire online normal force has effects on vehicle safety and performance and dynamic control systems. It is influenced by too many parameters such as vehicle mass and center of gravity position and vehicle instantaneous dynamics states. In this paper, a new estimation algorithm is developed to estimate tires’ online normal forces during a maneuver. The proposed algorithm uses a dynamic measure module to make a hardware-software coupled model which is validated by real test data. The algorithm uses artificial neural networks advantages to estimate the vehicle mass distributions. A combination of real and model-generated data is used to train, test, and validate the artificial neural network structure. By applying two roll and pitch artificial neural network blocks, it estimates tires’ static normal forces. In this respect, the validated vehicle model instantaneously monitors the estimated values. The results show that the proposed algorithm estimates the vehicle total mass with less than 5 percent. In addition, the coupled model uses the estimated static values to estimate the tire's online normal forces with considering the measured vehicle dynamics states by dynamic module. Comparing the obtained results from the proposed method with the outputs from Carsim indicates the acceptable accuracy of this method.

Keywords

Main Subjects

References

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Amirkabir Journal of Mechanical Engineering
Volume 54, Issue 2
May 2022
Pages 333-356

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