Estimation Strategy for the Adhesion Coefficient of Arbitrary Pavements Based on an Optimal Adaptive Fusion Algorithm
Accurately and quickly estimating the peak pavement adhesion coefficient is crucial for achieving high-quality driving and for optimizing vehicle stability control strategies. However, it also helps with putting forward higher requirements for vehicle driving states, tire model construction, the spe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/1/17 |
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| author | Zhiwei Xu Jianxi Wang Yongjie Lu Haoyu Li |
| author_facet | Zhiwei Xu Jianxi Wang Yongjie Lu Haoyu Li |
| author_sort | Zhiwei Xu |
| collection | DOAJ |
| description | Accurately and quickly estimating the peak pavement adhesion coefficient is crucial for achieving high-quality driving and for optimizing vehicle stability control strategies. However, it also helps with putting forward higher requirements for vehicle driving states, tire model construction, the speed of the convergence, and the precision of the estimation algorithm. This paper unequivocally presents two highly effective methods for accurately estimating the peak pavement adhesion coefficient. Firstly, a new dimensionless tire model is constructed. A relationship between the mechanical tire characteristics and peak adhesion coefficient is established by using the Burckhardt model’s analogy between the adhesion coefficient and peak adhesion coefficient, and the UKE algorithm completes the estimation. Secondly, an adaptive variable universe fuzzy algorithm (AVUFS) is established using the follow-up of the adhesion coefficient between the tire and the road surface. Even if the slip rate is less than 5%, the algorithm can still complete accurate estimations and does not depend on the initial given information. Finally, using the estimation advantages of the two algorithms, fusion optimization is performed, and the best estimation result is obtained. Based on the simulation results, the algorithm can quickly and precisely predict the maximum pavement adhesion coefficient in situations where the pavement has a low or high adhesion coefficient. |
| format | Article |
| id | doaj-art-fa5d8ee4ac4c401a9de980a49c9facc2 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-fa5d8ee4ac4c401a9de980a49c9facc22025-01-24T13:39:09ZengMDPI AGMachines2075-17022024-12-011311710.3390/machines13010017Estimation Strategy for the Adhesion Coefficient of Arbitrary Pavements Based on an Optimal Adaptive Fusion AlgorithmZhiwei Xu0Jianxi Wang1Yongjie Lu2Haoyu Li3School of Traffic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaState Key Laboratory of Mechanical Behavior in Traffic Engineering Structure and System Safety, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaState Key Laboratory of Mechanical Behavior in Traffic Engineering Structure and System Safety, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaAccurately and quickly estimating the peak pavement adhesion coefficient is crucial for achieving high-quality driving and for optimizing vehicle stability control strategies. However, it also helps with putting forward higher requirements for vehicle driving states, tire model construction, the speed of the convergence, and the precision of the estimation algorithm. This paper unequivocally presents two highly effective methods for accurately estimating the peak pavement adhesion coefficient. Firstly, a new dimensionless tire model is constructed. A relationship between the mechanical tire characteristics and peak adhesion coefficient is established by using the Burckhardt model’s analogy between the adhesion coefficient and peak adhesion coefficient, and the UKE algorithm completes the estimation. Secondly, an adaptive variable universe fuzzy algorithm (AVUFS) is established using the follow-up of the adhesion coefficient between the tire and the road surface. Even if the slip rate is less than 5%, the algorithm can still complete accurate estimations and does not depend on the initial given information. Finally, using the estimation advantages of the two algorithms, fusion optimization is performed, and the best estimation result is obtained. Based on the simulation results, the algorithm can quickly and precisely predict the maximum pavement adhesion coefficient in situations where the pavement has a low or high adhesion coefficient.https://www.mdpi.com/2075-1702/13/1/17peak adhesion coefficientvariable domaintire modelfull roadadaptive fusion |
| spellingShingle | Zhiwei Xu Jianxi Wang Yongjie Lu Haoyu Li Estimation Strategy for the Adhesion Coefficient of Arbitrary Pavements Based on an Optimal Adaptive Fusion Algorithm Machines peak adhesion coefficient variable domain tire model full road adaptive fusion |
| title | Estimation Strategy for the Adhesion Coefficient of Arbitrary Pavements Based on an Optimal Adaptive Fusion Algorithm |
| title_full | Estimation Strategy for the Adhesion Coefficient of Arbitrary Pavements Based on an Optimal Adaptive Fusion Algorithm |
| title_fullStr | Estimation Strategy for the Adhesion Coefficient of Arbitrary Pavements Based on an Optimal Adaptive Fusion Algorithm |
| title_full_unstemmed | Estimation Strategy for the Adhesion Coefficient of Arbitrary Pavements Based on an Optimal Adaptive Fusion Algorithm |
| title_short | Estimation Strategy for the Adhesion Coefficient of Arbitrary Pavements Based on an Optimal Adaptive Fusion Algorithm |
| title_sort | estimation strategy for the adhesion coefficient of arbitrary pavements based on an optimal adaptive fusion algorithm |
| topic | peak adhesion coefficient variable domain tire model full road adaptive fusion |
| url | https://www.mdpi.com/2075-1702/13/1/17 |
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