Details
| Original language | English |
|---|---|
| Title of host publication | 2019 18th European Control Conference, ECC 2019 |
| Subtitle of host publication | Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1239-1244 |
| Number of pages | 6 |
| ISBN (electronic) | 978-3-907144-00-8 |
| ISBN (print) | 978-1-7281-1314-2 |
| Publication status | Published - Jun 2019 |
| Event | 2019 European Control Conference (ECC) - Naples, Italy Duration: 25 Jun 2019 → 28 Jun 2019 |
Abstract
With the recent emergence of electric powertrains, a faster and easy to model actuator, the electric motor, became available for the control of longitudinal dynamics. Therefore model-based control approaches promise an increase in control performance, especially for processes such as traction control that require highly dynamic control intervention. The task of traction controllers is to prevent the driven wheels from slipping and thus ensure the vehicle's steerability. In this paper, a model predictive control approach to traction control is developed. A semi implicit method to discretize the underlying model was proposed to handle numerical stability problems at low speeds in real time. Due to changing environmental conditions, the functionality of the traction controller is limited and may lead to performance degradation or even failure. Therefore, a maximum friction coefficient estimation utilizing an unscentend Kalman filter is integrated. The overall control scheme is experimentally evaluated with a Volkswagen Golf GTE Plug-In Hybrid on a test track with a wet steel road surface.
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Instrumentation
- Mathematics(all)
- Control and Optimization
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2019 18th European Control Conference, ECC 2019: Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1239-1244 8795687.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Adaptive model predictive traction control for electric vehicles
AU - Busch, Alexander
AU - Wielitzka, Mark
AU - Ortmaier, Tobias
AU - Kleyman, Viktoria
N1 - The authors would like to thank the German federal ministry for economic affairs and energy (BMWi) and the German aerospace center (DLR) for founding this project.
PY - 2019/6
Y1 - 2019/6
N2 - With the recent emergence of electric powertrains, a faster and easy to model actuator, the electric motor, became available for the control of longitudinal dynamics. Therefore model-based control approaches promise an increase in control performance, especially for processes such as traction control that require highly dynamic control intervention. The task of traction controllers is to prevent the driven wheels from slipping and thus ensure the vehicle's steerability. In this paper, a model predictive control approach to traction control is developed. A semi implicit method to discretize the underlying model was proposed to handle numerical stability problems at low speeds in real time. Due to changing environmental conditions, the functionality of the traction controller is limited and may lead to performance degradation or even failure. Therefore, a maximum friction coefficient estimation utilizing an unscentend Kalman filter is integrated. The overall control scheme is experimentally evaluated with a Volkswagen Golf GTE Plug-In Hybrid on a test track with a wet steel road surface.
AB - With the recent emergence of electric powertrains, a faster and easy to model actuator, the electric motor, became available for the control of longitudinal dynamics. Therefore model-based control approaches promise an increase in control performance, especially for processes such as traction control that require highly dynamic control intervention. The task of traction controllers is to prevent the driven wheels from slipping and thus ensure the vehicle's steerability. In this paper, a model predictive control approach to traction control is developed. A semi implicit method to discretize the underlying model was proposed to handle numerical stability problems at low speeds in real time. Due to changing environmental conditions, the functionality of the traction controller is limited and may lead to performance degradation or even failure. Therefore, a maximum friction coefficient estimation utilizing an unscentend Kalman filter is integrated. The overall control scheme is experimentally evaluated with a Volkswagen Golf GTE Plug-In Hybrid on a test track with a wet steel road surface.
UR - http://www.scopus.com/inward/record.url?scp=85071540860&partnerID=8YFLogxK
U2 - 10.23919/ecc.2019.8795687
DO - 10.23919/ecc.2019.8795687
M3 - Conference contribution
AN - SCOPUS:85071540860
SN - 978-1-7281-1314-2
SP - 1239
EP - 1244
BT - 2019 18th European Control Conference, ECC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 European Control Conference (ECC)
Y2 - 25 June 2019 through 28 June 2019
ER -