A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution
In the study of braking force distribution of trucks, the accurate estimation of the state parameters of the vehicle is very critical. However, during the braking process, the state parameters of the vehicle present a highly nonlinear relationship that is difficult to estimate accurately and that se...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/21/8358 |
| _version_ | 1797466495098290176 |
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| author | Qunyi Chu Wen Sun Yuanjian Zhang |
| author_facet | Qunyi Chu Wen Sun Yuanjian Zhang |
| author_sort | Qunyi Chu |
| collection | DOAJ |
| description | In the study of braking force distribution of trucks, the accurate estimation of the state parameters of the vehicle is very critical. However, during the braking process, the state parameters of the vehicle present a highly nonlinear relationship that is difficult to estimate accurately and that seriously affects the accuracy of the braking force distribution strategy. To solve this problem, this paper proposes a machine-learning-based state-parameter estimation method to provide a solid data base for the braking force distribution strategy of the vehicle. Firstly, the actual collected complete vehicle information is processed for data; secondly, random forest is applied for the feature screening of data to reduce the data dimensionality; subsequently, the generalized regression neural network (GRNN) model is trained offline, and the vehicle state parameters are estimated online; the estimated parameters are used to implement the four-wheel braking force distribution strategy; finally, the effectiveness of the method is verified by joint simulation using MATLAB/Simulink and TruckSim. |
| first_indexed | 2024-03-09T18:40:36Z |
| format | Article |
| id | doaj.art-ebb909e3542d4782b5abcdbd634beda6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:40:36Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ebb909e3542d4782b5abcdbd634beda62023-11-24T06:46:46ZengMDPI AGSensors1424-82202022-10-012221835810.3390/s22218358A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force DistributionQunyi Chu0Wen Sun1Yuanjian Zhang2Arts and Sciences, New York University Shanghai, Shanghai 200122, ChinaCollege of Automotive Engineering, Changzhou Institute of Technology, Changzhou 213028, ChinaAeronautical and Automotive Engineering Department, Loughborough University, Loughborough LE11 3TT, UKIn the study of braking force distribution of trucks, the accurate estimation of the state parameters of the vehicle is very critical. However, during the braking process, the state parameters of the vehicle present a highly nonlinear relationship that is difficult to estimate accurately and that seriously affects the accuracy of the braking force distribution strategy. To solve this problem, this paper proposes a machine-learning-based state-parameter estimation method to provide a solid data base for the braking force distribution strategy of the vehicle. Firstly, the actual collected complete vehicle information is processed for data; secondly, random forest is applied for the feature screening of data to reduce the data dimensionality; subsequently, the generalized regression neural network (GRNN) model is trained offline, and the vehicle state parameters are estimated online; the estimated parameters are used to implement the four-wheel braking force distribution strategy; finally, the effectiveness of the method is verified by joint simulation using MATLAB/Simulink and TruckSim.https://www.mdpi.com/1424-8220/22/21/8358state estimationdata processingfeature filteringbraking force distribution strategygeneralized regression neural network (GRNN) |
| spellingShingle | Qunyi Chu Wen Sun Yuanjian Zhang A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution Sensors state estimation data processing feature filtering braking force distribution strategy generalized regression neural network (GRNN) |
| title | A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution |
| title_full | A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution |
| title_fullStr | A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution |
| title_full_unstemmed | A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution |
| title_short | A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution |
| title_sort | data driven method for the estimation of truck state parameters and braking force distribution |
| topic | state estimation data processing feature filtering braking force distribution strategy generalized regression neural network (GRNN) |
| url | https://www.mdpi.com/1424-8220/22/21/8358 |
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