A PCA-based Modeling Approach for Estimation of Road-tire Forces by In-tire Accelerometers

Krier, David; Zanardo, Gabriele; Re, Luigi del

doi:10.3182/20140824-6-za-1003.02293

Cited by 18 publications

(5 citation statements)

References 11 publications

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“…However, the most accurate measurement device is represented by dynamo metric wheels Blasco et al (2014), whose diffusion is limited by economic and space requirements. Other interesting solutions under development are represented by load sensing bearings Van Leeuwen et al (2005), Madhusudhanan et al (2016a) and in-tire sensors Krier et al (2014), which are not ready for commercial production. As a consequence, the problem of estimating tire forces has gained a lot of attention in the last decades.…”

Section: Introductionmentioning

confidence: 99%

Twin-in-the-loop state estimation for vehicle dynamics control: theory and experiments

Riva¹,

Formentin²,

Corno³

et al. 2022

Preprint

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In vehicle dynamics control, many variables of interest cannot be directly measured, as sensors might be costly, fragile or even not available. Therefore, real-time estimation techniques need to be used. The previous approach suffers from two main drawbacks: (i) the approximations due to model mismatch might jeopardize the performance of the final estimation-based control; (ii) each new estimator requires the calibration from scratch of a dedicated model. In this paper, we propose a simulator-in-the-loop scheme, where the adhoc model is replaced by an accurate multibody simulator of the vehicle, typically available to vehicles manufacturers and suitable for the estimation of any on-board variable, coupled with a compensator within a closed-loop observer scheme. Given the black-box nature of the simulator, a data-driven methodology for observer tuning is developed, based on Bayesian optimization. The effectiveness of the proposed estimation method for the estimation of vehicle states and forces, as compared to traditional model-based Kalman filtering, is experimentally shown on a dataset collected with a sport car.

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Section: Introductionmentioning

confidence: 99%

Twin-in-the-loop state estimation for vehicle dynamics control: theory and experiments

Riva¹,

Formentin²,

Corno³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The tire force is the fundamental cause of changing the vehicle motion state, the tire force is the basis for controlling the vehicle state, and there is a transparent mapping relationship between the tire force and the vehicle state [ 16 , 17 ]. There are three main traditional methods for tire force observation: filter-based methods [ 18 , 19 ], mathematical model-based methods [ 20 , 21 ], and sensor-based observation methods [ 22 , 23 , 24 ]. The filter-based methods are mainly Kalman filter and least squares [ 24 , 25 ], but this method is significantly affected by environmental factors in practical application, and the observation accuracy depends on the accuracy of the specified tire model.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution

Chu

Sun

Zhang

2022

Sensors

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In the study of braking force distribution of trucks, the accurate estimation of the state parameters of the vehicle is very critical. However, during the braking process, the state parameters of the vehicle present a highly nonlinear relationship that is difficult to estimate accurately and that seriously affects the accuracy of the braking force distribution strategy. To solve this problem, this paper proposes a machine-learning-based state-parameter estimation method to provide a solid data base for the braking force distribution strategy of the vehicle. Firstly, the actual collected complete vehicle information is processed for data; secondly, random forest is applied for the feature screening of data to reduce the data dimensionality; subsequently, the generalized regression neural network (GRNN) model is trained offline, and the vehicle state parameters are estimated online; the estimated parameters are used to implement the four-wheel braking force distribution strategy; finally, the effectiveness of the method is verified by joint simulation using MATLAB/Simulink and TruckSim.

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“…The basic principle of the acceleration sensor application is that when the tire rotates, the acceleration sensor fixed in different parts of the tire will collect the three-axis acceleration signal generated by the tire deformation in real time, and after analyzing and processing the signal, the change characteristics can be connected with the grounding characteristics under different working conditions of the tire. According to this, the purpose of tire state estimation can be achieved by different algorithms [15][16][17]. Federico C takes the vertical acceleration in the three-axis acceleration signal as the characteristic parameter and identifies the length of tire ground mark according to the peak value in the vertical acceleration signal curve.…”

Section: Introductionmentioning

confidence: 99%

Research on load prediction model construction method of the tire condition monitoring system

Qiang

et al. 2021

J Braz. Soc. Mech. Sci. Eng.

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Combined with the concept of intelligent tire, a tire condition monitoring system for predicting tire load information was designed, and a construction method of the tire load prediction model was proposed. Based on the finite element method, the construction process of tire load prediction model was explored. Under different inflation pressure and radial force, the tire deflection, the variation of tread height and tire cavity height in the grounding center area were obtained. Based on the above data, the tire radial stiffness fitting model and tread height variation fitting model were established. Finally, according to the theoretical formula of tire radial stiffness, the tire load prediction model was established and its validity was verified. The results show that the prediction model has high accuracy and can meet the requirements of use, which provides a theoretical basis for the development and design of tire condition monitoring system.

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A PCA-based Modeling Approach for Estimation of Road-tire Forces by In-tire Accelerometers

Cited by 18 publications

References 11 publications

Twin-in-the-loop state estimation for vehicle dynamics control: theory and experiments

Twin-in-the-loop state estimation for vehicle dynamics control: theory and experiments

A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution

Research on load prediction model construction method of the tire condition monitoring system

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