Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105324
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | - |
dc.creator | Wu, F | - |
dc.creator | Sun, C | - |
dc.creator | Li, H | - |
dc.creator | Zheng, S | - |
dc.date.accessioned | 2024-04-12T06:51:40Z | - |
dc.date.available | 2024-04-12T06:51:40Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105324 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Wu F, Sun C, Li H, Zheng S. Real-Time Center of Gravity Estimation for Intelligent Connected Vehicle Based on HEKF-EKF. Electronics. 2023; 12(2):386 is available at https://doi.org/10.3390/electronics12020386. | en_US |
dc.subject | Center of gravity | en_US |
dc.subject | HEKF-EKF | en_US |
dc.subject | Intelligent connected vehicle | en_US |
dc.subject | Parameter estimation | en_US |
dc.title | Real-time center of gravity estimation for intelligent connected vehicle based on HEKF-EKF | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 12 | - |
dc.identifier.issue | 2 | - |
dc.identifier.doi | 10.3390/electronics12020386 | - |
dcterms.abstract | The vehicle center of gravity estimation is the key technology to the vehicle active safety system in intelligent connected vehicles. In this study, an integrated estimation approach for center of gravity (CG) combining Huber Extended Kalman Filter and Extended Kalman Filter (HEKF-EKF) is proposed. First, HEKF algorithm is used to estimate the distance between the CG and the front axle at the current time. Then, the CG height obtained by HEKF and EKF algorithms is weighted to obtain the optimal estimate value. Finally, the results show that the algorithm’s estimation convergence time is 2 s, its longitudinal position estimation error is less than 2%, and its center of gravity height estimation error is less than 3%. The longitudinal and vertical positions of the vehicle CG can be accurately estimated using this method. This method can help advance the development of active safety technology. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Electronics (Switzerland), Jan. 2023, v. 12, no. 2, 386 | - |
dcterms.isPartOf | Electronics (Switzerland) | - |
dcterms.issued | 2023-01 | - |
dc.identifier.scopus | 2-s2.0-85146743579 | - |
dc.identifier.eissn | 2079-9292 | - |
dc.identifier.artn | 386 | - |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Opening Fund of Key Laboratory of Transportation Industry of Automotive Transportation Safety Enhancement Technology (Chang’an University), PRC; Hubei Science and Technology Project; Suzhou Industrial Prospect and Key Technology Project; Natural Science Foundation of Jiangsu Province; Hong Kong Scholars Program | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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electronics-12-00386.pdf | 8 MB | Adobe PDF | View/Open |
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