| contributor author | Tang, Jian | |
| contributor author | Dourra, Hussein | |
| contributor author | Zhu, Guoming | |
| date accessioned | 2023-08-16T18:34:57Z | |
| date available | 2023-08-16T18:34:57Z | |
| date copyright | 4/27/2023 12:00:00 AM | |
| date issued | 2023 | |
| identifier issn | 2689-6117 | |
| identifier other | aldsc_3_1_011006.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4292166 | |
| description abstract | The tire-road interaction generates vehicle driving forces, which affect vehicle performance such as maximum acceleration and stability. Sequential extended Kalman filter (S-EKF) integrated with a slope method has been used for tire-road friction coefficient estimation with its own limitations, along with several “cause-based” and “effect-based” methods. This research proposes a new stochastic-based evaluation criterion using existing vehicle sensor signals with the help of the data-driven Kriging model. The proposed estimation method is validated by both CarSim™ simulation and experimental studies, respectively, under different road conditions. The results show that the proposed novel criterion has a strong correlation with the road friction coefficient and provide an improved tire-road friction coefficient estimation. A signal fusion estimation scheme based on both S-EKF and proposed evaluations is developed to improve estimation robustness. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Tire-Road Friction Coefficient Estimation Based on Fusion of Model- and Data-Based Methods | |
| type | Journal Paper | |
| journal volume | 3 | |
| journal issue | 1 | |
| journal title | ASME Letters in Dynamic Systems and Control | |
| identifier doi | 10.1115/1.4062283 | |
| journal fristpage | 11006-1 | |
| journal lastpage | 11006-7 | |
| page | 7 | |
| tree | ASME Letters in Dynamic Systems and Control:;2023:;volume( 003 ):;issue: 001 | |
| contenttype | Fulltext | |
