Comprehensive tire–road friction coefficient estimation based on signal fusion method under complex maneuvering operations

Zhang, Lipeng; Yang, Kaiming; Jia, Gang; Xu, Ruidong; Song, Jingyan; Han, Z.-Q.

doi:10.1016/j.ymssp.2014.10.006

Cited by 101 publications

(43 citation statements)

References 25 publications

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“…Finally, the one-directional friction coefficients are obtained from the product of these weighting functions and the normalised forces calculated previously, expression (20).…”

Section: Magic Formulamentioning

confidence: 99%

“…The majority of the solutions proposed in the literature are predominantly model-based approaches [8,23,34,43,[98][99][100][101][102][103][104][105][106][107][108][109][110][111], and differ depending on the technical solution adopted to infer the friction potential from the parameterised friction or tyre force model (e.g., Unscented Kalman filter (UKF) [8] or Bayesian Hypothesis selection [43,111]). Apart from this dominant group, additional solutions have been proposed using data-based [3,112], rule-based [20], self-excitation [113], or wheel acceleration-based approaches [114]. These will be briefly discussed in the following.…”

Section: Lateral Dynamicsmentioning

confidence: 99%

“…Despite the fact that it is indicated that the friction potential can be calculated from the friction consumption level additional results in this line are not provided. A tyre model-less approach was presented by Li et al in [20]. In this case, the grip utilisation levels during braking µ abs , driving µ tcs , and cornering µ ayc manoeuvres are employed in a signal fusion fashion to obtain a "comprehensive" road friction estimate (µ comp ), defined as…”

Section: Lateral Dynamicsmentioning

confidence: 99%

“…An important drawback remarked by several authors is that a high excitation level (e.g., an emergency braking manoeuvre) is necessary in order to obtain an accurate estimation of the road grip potential when traditional slip-based approaches are employed [15][16][17][18]. Nevertheless, as the previous systems are triggered when a significant excitation is present, the friction coefficient can be estimated during the system intervention [19,20] and the system thresholds can be readjusted in parallel.…”

Section: Introductionmentioning

confidence: 99%

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Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

2017

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Abstract:In this paper, a review on road friction virtual sensing approaches is provided. In particular, this work attempts to address whether the road grip potential can be estimated accurately under regular driving conditions in which the vehicle responses remain within low longitudinal and lateral excitation levels. This review covers in detail the most relevant effect-based estimation methods; these are methods in which the road friction characteristics are inferred from the tyre responses: tyre slip, tyre vibration, and tyre noise. Slip-based approaches (longitudinal dynamics, lateral dynamics, and tyre self-alignment moment) are covered in the first part of the review, while low frequency and high frequency vibration-based works are presented in the following sections. Finally, a brief summary containing the main advantages and drawbacks derived from each estimation method and the future envisaged research lines are presented in the last sections of the paper.

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“…Finally, the one-directional friction coefficients are obtained from the product of these weighting functions and the normalised forces calculated previously, expression (20).…”

Section: Magic Formulamentioning

confidence: 99%

Section: Lateral Dynamicsmentioning

confidence: 99%

Section: Lateral Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

2017

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“…However, neural networks are computationally expensive. In a recent study, references [5] and [6] use the longitudinal tire force and the signal fusion method to obtain the car tire-road friction coefficient, respectively. These methods, however, all focus on the study of the friction behavior on car tires.…”

Section: Introductionmentioning

confidence: 99%

Friction Signal Denoising Using Complete Ensemble EMD with Adaptive Noise and Mutual Information

Zhan

Shen

2015

Entropy

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During the measurement of friction force, the measured signal generally contains noise. To remove the noise and preserve the important features of the signal, a hybrid filtering method is introduced that uses the mutual information and a new waveform. This new waveform is the difference between the original signal and the sum of intrinsic mode functions (IMFs), which are obtained by empirical mode decomposition (EMD) or its improved versions. To evaluate the filter performance for the friction signal, ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), and complete ensemble EMD with adaptive noise (CEEMDAN) are employed in combination with the proposed filtering method. The combination is used to filter the synthesizing signals at first. For the filtering of the simulation signal, the filtering effect is compared under conditions of different ensemble number, sampling frequency, and the input signal-noise ratio, respectively. Results show that CEEMDAN outperforms other signal filtering methods. In particular, this method is successful in filtering the friction signal as evaluated by the de-trended fluctuation analysis (DFA) algorithm.

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Neural Network-Based Road Surface Condition Monitor Concept

Dobaj

2023

Applied Condition Monitoring

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Comprehensive tire–road friction coefficient estimation based on signal fusion method under complex maneuvering operations

Cited by 101 publications

References 25 publications

Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

Friction Signal Denoising Using Complete Ensemble EMD with Adaptive Noise and Mutual Information

Neural Network-Based Road Surface Condition Monitor Concept

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