Vehicle State Estimation Based on Multidimensional Information Fusion
As the core input parameters of various control systems, the real-time and accurate acquisition of reference speed, mass and road slope is the key factor to improve the performance of intelligent vehicle dynamics control. Therefore, the parameter estimation method based on multi-dimensional informat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9832610/ |
| _version_ | 1798040408960270336 |
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| author | Duanyang Tian Liqiang Jin Zhihui Zhang Hao Li |
| author_facet | Duanyang Tian Liqiang Jin Zhihui Zhang Hao Li |
| author_sort | Duanyang Tian |
| collection | DOAJ |
| description | As the core input parameters of various control systems, the real-time and accurate acquisition of reference speed, mass and road slope is the key factor to improve the performance of intelligent vehicle dynamics control. Therefore, the parameter estimation method based on multi-dimensional information fusion is proposed in this paper. A comprehensive evaluation of wheel dynamics state is realized by information fusion, which is quantified in terms of wheel speed credibility. Then the calculated dynamic speed and auxiliary speed are weighted coupled to achieve accurate estimation of reference speed, which avoids the influence of unstable wheels. Similarly, the method to calculate the confidence factor of mass estimation is established in order to screen the vehicle state suitable for estimation. And the online estimation of mass is realized based on recursive least square method. Meanwhile, the road slope estimation algorithm based on interactive multiple model has been designed, which achieves the weighted fusion of Kalman filter observer based on kinematics and unscented Kalman filter observer based on dynamics. Finally, the road tests were carried out on representative working conditions. The maximum error between the actual speed and the reference speed does not exceed 0.68m/s. The relative error of mass estimation is not more than 1.95%, and the absolute error of slope estimation is less than 1.84%, which proves that the proposed estimation algorithm has high comprehensive performance. More importantly, it is not limited to specific working conditions, which means a great significance for the development of intelligent vehicles. |
| first_indexed | 2024-04-11T22:07:13Z |
| format | Article |
| id | doaj.art-8a2fd9c39cc6450ca838c161dd89360b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T22:07:13Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8a2fd9c39cc6450ca838c161dd89360b2022-12-22T04:00:40ZengIEEEIEEE Access2169-35362022-01-0110762207623210.1109/ACCESS.2022.31921249832610Vehicle State Estimation Based on Multidimensional Information FusionDuanyang Tian0https://orcid.org/0000-0001-7011-9355Liqiang Jin1https://orcid.org/0000-0002-0545-3872Zhihui Zhang2Hao Li3State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaKey Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaAs the core input parameters of various control systems, the real-time and accurate acquisition of reference speed, mass and road slope is the key factor to improve the performance of intelligent vehicle dynamics control. Therefore, the parameter estimation method based on multi-dimensional information fusion is proposed in this paper. A comprehensive evaluation of wheel dynamics state is realized by information fusion, which is quantified in terms of wheel speed credibility. Then the calculated dynamic speed and auxiliary speed are weighted coupled to achieve accurate estimation of reference speed, which avoids the influence of unstable wheels. Similarly, the method to calculate the confidence factor of mass estimation is established in order to screen the vehicle state suitable for estimation. And the online estimation of mass is realized based on recursive least square method. Meanwhile, the road slope estimation algorithm based on interactive multiple model has been designed, which achieves the weighted fusion of Kalman filter observer based on kinematics and unscented Kalman filter observer based on dynamics. Finally, the road tests were carried out on representative working conditions. The maximum error between the actual speed and the reference speed does not exceed 0.68m/s. The relative error of mass estimation is not more than 1.95%, and the absolute error of slope estimation is less than 1.84%, which proves that the proposed estimation algorithm has high comprehensive performance. More importantly, it is not limited to specific working conditions, which means a great significance for the development of intelligent vehicles.https://ieeexplore.ieee.org/document/9832610/Vehicle state estimationmulti-information fusionreference speedsteady state evaluation |
| spellingShingle | Duanyang Tian Liqiang Jin Zhihui Zhang Hao Li Vehicle State Estimation Based on Multidimensional Information Fusion IEEE Access Vehicle state estimation multi-information fusion reference speed steady state evaluation |
| title | Vehicle State Estimation Based on Multidimensional Information Fusion |
| title_full | Vehicle State Estimation Based on Multidimensional Information Fusion |
| title_fullStr | Vehicle State Estimation Based on Multidimensional Information Fusion |
| title_full_unstemmed | Vehicle State Estimation Based on Multidimensional Information Fusion |
| title_short | Vehicle State Estimation Based on Multidimensional Information Fusion |
| title_sort | vehicle state estimation based on multidimensional information fusion |
| topic | Vehicle state estimation multi-information fusion reference speed steady state evaluation |
| url | https://ieeexplore.ieee.org/document/9832610/ |
| work_keys_str_mv | AT duanyangtian vehiclestateestimationbasedonmultidimensionalinformationfusion AT liqiangjin vehiclestateestimationbasedonmultidimensionalinformationfusion AT zhihuizhang vehiclestateestimationbasedonmultidimensionalinformationfusion AT haoli vehiclestateestimationbasedonmultidimensionalinformationfusion |