Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation
In this paper, vehicle mass estimation problem was researched by the Kalman Filtering process to investigate the effectiveness of Extended Kalman Filter technique on mass estimation, which has widely been studied on the related literature with all the successful results. For the purpose, a longit...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Bursa Technical University
2021-06-01
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| Series: | Journal of Innovative Science and Engineering |
| Subjects: | |
| Online Access: | http://jise.btu.edu.tr/en/download/article-file/1488383 |
| _version_ | 1797919775081365504 |
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| author | Mert BÜYÜKKÖPRÜ Erdem UZUNSOY |
| author_facet | Mert BÜYÜKKÖPRÜ Erdem UZUNSOY |
| author_sort | Mert BÜYÜKKÖPRÜ |
| collection | DOAJ |
| description | In this paper, vehicle mass estimation problem was researched by the Kalman Filtering
process to investigate the effectiveness of Extended Kalman Filter technique on mass
estimation, which has widely been studied on the related literature with all the successful
results. For the purpose, a longitudinal vehicle dynamics model was developed, and the
equations were transformed into the well-known state space form. Nonlinear problem in
its nature was discretised and linearized by using Euler method. In addition, road slope
was calculated by a road slope inclinometer logic. IPG CarMaker® was utilized to
overcome the nonlinearities, design the test environment and the conditions, as it was
observed that the simulation results were not met the expectations with the logged
vehicle physical test data. Several test scenarios were run to simulate level and wavy
road with different vehicle speeds. The results showed that the Extended Kalman Filter
could provide a reasonable convergence when the initial values were correctly selected,
no nonlinear issue occurred and certain tuning parameters were defined with stable
boundary conditions for mass estimation. |
| first_indexed | 2024-04-10T13:52:00Z |
| format | Article |
| id | doaj.art-4cc415ab8b194013a23c8928c1aead93 |
| institution | Directory Open Access Journal |
| issn | 2602-4217 2602-4217 |
| language | English |
| last_indexed | 2024-04-10T13:52:00Z |
| publishDate | 2021-06-01 |
| publisher | Bursa Technical University |
| record_format | Article |
| series | Journal of Innovative Science and Engineering |
| spelling | doaj.art-4cc415ab8b194013a23c8928c1aead932023-02-15T16:10:40ZengBursa Technical UniversityJournal of Innovative Science and Engineering2602-42172602-42172021-06-015111110.38088/jise.755616Reliability of Extended Kalman Filtering Technic on Vehicle Mass EstimationMert BÜYÜKKÖPRÜ0https://orcid.org/0000-0003-3493-8323Erdem UZUNSOY1https://orcid.org/0000-0002-6449-552XGroupe Renault, Bursa, TurkeyBursa Technical University, Mechanical Engineering Department, 16310, Bursa, TurkeyIn this paper, vehicle mass estimation problem was researched by the Kalman Filtering process to investigate the effectiveness of Extended Kalman Filter technique on mass estimation, which has widely been studied on the related literature with all the successful results. For the purpose, a longitudinal vehicle dynamics model was developed, and the equations were transformed into the well-known state space form. Nonlinear problem in its nature was discretised and linearized by using Euler method. In addition, road slope was calculated by a road slope inclinometer logic. IPG CarMaker® was utilized to overcome the nonlinearities, design the test environment and the conditions, as it was observed that the simulation results were not met the expectations with the logged vehicle physical test data. Several test scenarios were run to simulate level and wavy road with different vehicle speeds. The results showed that the Extended Kalman Filter could provide a reasonable convergence when the initial values were correctly selected, no nonlinear issue occurred and certain tuning parameters were defined with stable boundary conditions for mass estimation.http://jise.btu.edu.tr/en/download/article-file/1488383extended kalman filtermass estimationvehicle longitudinal dynamics |
| spellingShingle | Mert BÜYÜKKÖPRÜ Erdem UZUNSOY Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation Journal of Innovative Science and Engineering extended kalman filter mass estimation vehicle longitudinal dynamics |
| title | Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation |
| title_full | Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation |
| title_fullStr | Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation |
| title_full_unstemmed | Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation |
| title_short | Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation |
| title_sort | reliability of extended kalman filtering technic on vehicle mass estimation |
| topic | extended kalman filter mass estimation vehicle longitudinal dynamics |
| url | http://jise.btu.edu.tr/en/download/article-file/1488383 |
| work_keys_str_mv | AT mertbuyukkopru reliabilityofextendedkalmanfilteringtechniconvehiclemassestimation AT erdemuzunsoy reliabilityofextendedkalmanfilteringtechniconvehiclemassestimation |