Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2017 American Control Conference, ACC 2017 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 4322-4327 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781509059928 |
| Publikationsstatus | Veröffentlicht - 29 Juni 2017 |
| Veranstaltung | 2017 American Control Conference, ACC 2017 - Seattle, USA / Vereinigte Staaten Dauer: 24 Mai 2017 → 26 Mai 2017 |
Publikationsreihe
| Name | Proceedings of the American Control Conference |
|---|---|
| Band | 0 |
| ISSN (Print) | 0743-1619 |
Abstract
Advanced driver assistance systems in modern vehicles have gained interest in the past decades. Most of these systems rely decisively on knowledge of the vehicle's state and influential parameters. Due to changing system or environmental conditions the functionality of these systems may lead to decreased performance or even failure. Especially, the road condition, represented by the maximum friction coefficient, essentially influencing the interaction of tires and road, has major influence on the vehicle's behavior. Therefore, a vast improvement of the assistance systems' performance can be achieved by online maximum friction coefficient estimation. In this paper a simultaneous online estimation of the vehicle's state and maximum friction coefficient is presented using a joint Unscented Kalman Filter. The state and friction estimation results are validated by comparing to measurements taken on a Volkswagen Golf GTE Plug-In Hybrid and offline identified values, respectively.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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2017 American Control Conference, ACC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. S. 4322-4327 7963620 (Proceedings of the American Control Conference; Band 0).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - State and maximum friction coefficient estimation in vehicle dynamics using UKF
AU - Wielitzka, Mark
AU - Dagen, Matthias
AU - Ortmaier, Tobias
PY - 2017/6/29
Y1 - 2017/6/29
N2 - Advanced driver assistance systems in modern vehicles have gained interest in the past decades. Most of these systems rely decisively on knowledge of the vehicle's state and influential parameters. Due to changing system or environmental conditions the functionality of these systems may lead to decreased performance or even failure. Especially, the road condition, represented by the maximum friction coefficient, essentially influencing the interaction of tires and road, has major influence on the vehicle's behavior. Therefore, a vast improvement of the assistance systems' performance can be achieved by online maximum friction coefficient estimation. In this paper a simultaneous online estimation of the vehicle's state and maximum friction coefficient is presented using a joint Unscented Kalman Filter. The state and friction estimation results are validated by comparing to measurements taken on a Volkswagen Golf GTE Plug-In Hybrid and offline identified values, respectively.
AB - Advanced driver assistance systems in modern vehicles have gained interest in the past decades. Most of these systems rely decisively on knowledge of the vehicle's state and influential parameters. Due to changing system or environmental conditions the functionality of these systems may lead to decreased performance or even failure. Especially, the road condition, represented by the maximum friction coefficient, essentially influencing the interaction of tires and road, has major influence on the vehicle's behavior. Therefore, a vast improvement of the assistance systems' performance can be achieved by online maximum friction coefficient estimation. In this paper a simultaneous online estimation of the vehicle's state and maximum friction coefficient is presented using a joint Unscented Kalman Filter. The state and friction estimation results are validated by comparing to measurements taken on a Volkswagen Golf GTE Plug-In Hybrid and offline identified values, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85026999855&partnerID=8YFLogxK
U2 - 10.23919/acc.2017.7963620
DO - 10.23919/acc.2017.7963620
M3 - Conference contribution
AN - SCOPUS:85026999855
T3 - Proceedings of the American Control Conference
SP - 4322
EP - 4327
BT - 2017 American Control Conference, ACC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 American Control Conference, ACC 2017
Y2 - 24 May 2017 through 26 May 2017
ER -