收藏切换
Estimation of Road Adhesion Coefficient Using Interactive Multiple Model Adaptive Unscented Kalman Filter for 4WID Vehicles
收藏切换
PDF
Haonan Deng1, Zhiguo Zhao1, Kun Zhao1, Gang Li2, Qin Yu1
Automotive Engineering | 2024, 46(8) : 1357 - 1369
Less
收藏切换
Automotive Engineering | 2024, 46(8): 1357-1369
Estimation of Road Adhesion Coefficient Using Interactive Multiple Model Adaptive Unscented Kalman Filter for 4WID Vehicles
Full
Haonan Deng1, Zhiguo Zhao1, Kun Zhao1, Gang Li2, Qin Yu1
Affiliations
  • 1. School of Automotive Studies,Tongji University,Shanghai  201804
  • 2. Lotus Automobile Company limited,Wuhan  430000
Published: 2024-08-25 doi: 10.19562/j.chinasae.qcgc.2024.08.003
Outline
收藏切换

The road adhesion coefficient has an important impact on the vehicle dynamics control performance. In order to accurately obtain the road adhesion coefficient in real time and improve the estimation accuracy and convergence speed of the algorithm under different road surfaces and driving conditions, an interactive multiple model adaptive unscented Kalman filter (IMM-AUKF) based on the seven-degree-of-freedom vehicle dynamics model and Dugoff tire model is proposed in this paper for the distributed four-wheel-drive vehicles. The algorithm first introduces the improved Sage-Husa noise estimator into the UKF algorithm to construct the AUKF observer, which updates the measurement noise in real time and ensures the positive characterization of its covariance matrix, improves the weight of the new observation data, and enhances the real-time tracking accuracy and stability of the algorithm. Afterwards, the algorithm selects different observation variables to construct the longitudinal driving condition AUKF observer and the lateral-longitudinal coupling driving condition AUKF observer. And the IMM algorithm is also used to switch the observer model, so as to realize the algorithm's accurate estimation of the road adhesion coefficient under different driving conditions. The results of simulation tests on high/low attachment, joint and u-split roads and real vehicle road tests show that the proposed IMM-AUKF algorithm has higher estimation accuracy and faster convergence speed than the traditional UKF algorithm, and it can adapt to the real-time and accurate estimation of the road adhesion coefficient under different driving conditions.

distributed four-wheel drive  /  road adhesion coefficient  /  interactive multiple model  /  adaptive unscented Kalman filter
Haonan Deng, Zhiguo Zhao, Kun Zhao, Gang Li, Qin Yu. Estimation of Road Adhesion Coefficient Using Interactive Multiple Model Adaptive Unscented Kalman Filter for 4WID Vehicles[J]. Automotive Engineering, 2024 , 46 (8) : 1357 -1369 . DOI: 10.19562/j.chinasae.qcgc.2024.08.003
Year 2024 volume 46 Issue 8
PDF
347
150
Cite this Article
BibTeX
Article Info
doi: 10.19562/j.chinasae.qcgc.2024.08.003
  • Receive Date:2023-12-29
  • Online Date:2025-07-29
  • Published:2024-08-25
Article Data
Affiliations
History
  • Received:2023-12-29
  • Revised:2024-02-20
Funding
Affiliations
    1. School of Automotive Studies,Tongji University,Shanghai  201804
    2. Lotus Automobile Company limited,Wuhan  430000
References
Share
https://castjournals.cast.org.cn/joweb/qcygc/EN/10.19562/j.chinasae.qcgc.2024.08.003
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT