Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman Algorithm
With regard to the rear-drive in-wheel motor vehicle, this paper studies the joint estimation method for the vehicle state and road adhesion coefficient. A nonlinear seven degrees of freedom vehicle estimation model and a tire estimation model are established. A vehicle driving state estimator and a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/19/10734 |
| _version_ | 1797576287564333056 |
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| author | Lingxiao Quan Ronglei Chang Changhong Guo |
| author_facet | Lingxiao Quan Ronglei Chang Changhong Guo |
| author_sort | Lingxiao Quan |
| collection | DOAJ |
| description | With regard to the rear-drive in-wheel motor vehicle, this paper studies the joint estimation method for the vehicle state and road adhesion coefficient. A nonlinear seven degrees of freedom vehicle estimation model and a tire estimation model are established. A vehicle driving state estimator and a road adhesion coefficient estimator based on the generalized high-order cubature Kalman filter (GHCKF) algorithm are designed. The vehicle state estimator combines the vehicle model and the tire model to calculate the vehicle state parameters, provides the state parameters for the road adhesion coefficient estimator, and realizes the real-time estimation of the road adhesion coefficient. The exponential fading memory adaptive algorithm is used to update the measurement noise variance, and we upgrade the GHCKF to the adaptive generalized high-order cubature Kalman filter (AGHCKF), which estimates the vehicle state and road adhesion coefficient. The typical working conditions using the double GHCKF/AGHCKF estimation algorithm were simulated and analyzed. Then, high-and low-speed driving experiments based on typical working conditions were carried out. An integrated navigation system (INS), global positioning system (GPS), and real-time kinematic positioning (RTK) were used to collect the real-time data of the vehicle, and compare them with the estimated values of the joint estimator, to verify the feasibility of the vehicle-state–road-adhesion-coefficient joint estimator. We compared a high-order GHCKF algorithm, high-order improved AGHCKF algorithm, and a cubature Kalman filter (CKF) algorithm, and the simulation and experimental results show that the joint estimator using the CKF, GHCKF, and AGHCKF algorithms can realize the real-time estimation of the vehicle state and the road adhesion coefficient. The AGHCKF algorithm shows the best effectiveness and robustness of the three algorithms. |
| first_indexed | 2024-03-10T21:50:06Z |
| format | Article |
| id | doaj.art-ede30aebdfd04a588736457ea40b0cb7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:50:06Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ede30aebdfd04a588736457ea40b0cb72023-11-19T14:03:28ZengMDPI AGApplied Sciences2076-34172023-09-0113191073410.3390/app131910734Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman AlgorithmLingxiao Quan0Ronglei Chang1Changhong Guo2School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, ChinaWith regard to the rear-drive in-wheel motor vehicle, this paper studies the joint estimation method for the vehicle state and road adhesion coefficient. A nonlinear seven degrees of freedom vehicle estimation model and a tire estimation model are established. A vehicle driving state estimator and a road adhesion coefficient estimator based on the generalized high-order cubature Kalman filter (GHCKF) algorithm are designed. The vehicle state estimator combines the vehicle model and the tire model to calculate the vehicle state parameters, provides the state parameters for the road adhesion coefficient estimator, and realizes the real-time estimation of the road adhesion coefficient. The exponential fading memory adaptive algorithm is used to update the measurement noise variance, and we upgrade the GHCKF to the adaptive generalized high-order cubature Kalman filter (AGHCKF), which estimates the vehicle state and road adhesion coefficient. The typical working conditions using the double GHCKF/AGHCKF estimation algorithm were simulated and analyzed. Then, high-and low-speed driving experiments based on typical working conditions were carried out. An integrated navigation system (INS), global positioning system (GPS), and real-time kinematic positioning (RTK) were used to collect the real-time data of the vehicle, and compare them with the estimated values of the joint estimator, to verify the feasibility of the vehicle-state–road-adhesion-coefficient joint estimator. We compared a high-order GHCKF algorithm, high-order improved AGHCKF algorithm, and a cubature Kalman filter (CKF) algorithm, and the simulation and experimental results show that the joint estimator using the CKF, GHCKF, and AGHCKF algorithms can realize the real-time estimation of the vehicle state and the road adhesion coefficient. The AGHCKF algorithm shows the best effectiveness and robustness of the three algorithms.https://www.mdpi.com/2076-3417/13/19/10734vehicle state estimatorroad adhesion coefficient estimatorjoint estimationthe exponential fading memoryGHCKFAGHCKF |
| spellingShingle | Lingxiao Quan Ronglei Chang Changhong Guo Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman Algorithm Applied Sciences vehicle state estimator road adhesion coefficient estimator joint estimation the exponential fading memory GHCKF AGHCKF |
| title | Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman Algorithm |
| title_full | Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman Algorithm |
| title_fullStr | Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman Algorithm |
| title_full_unstemmed | Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman Algorithm |
| title_short | Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman Algorithm |
| title_sort | vehicle state and road adhesion coefficient joint estimation based on high order cubature kalman algorithm |
| topic | vehicle state estimator road adhesion coefficient estimator joint estimation the exponential fading memory GHCKF AGHCKF |
| url | https://www.mdpi.com/2076-3417/13/19/10734 |
| work_keys_str_mv | AT lingxiaoquan vehiclestateandroadadhesioncoefficientjointestimationbasedonhighordercubaturekalmanalgorithm AT rongleichang vehiclestateandroadadhesioncoefficientjointestimationbasedonhighordercubaturekalmanalgorithm AT changhongguo vehiclestateandroadadhesioncoefficientjointestimationbasedonhighordercubaturekalmanalgorithm |