A comparative study on Optimal Control based torque vectoring systems

Asier Alonso; Alberto Parra; Asier Zubizarreta; Inaki Sainz

doi:10.1109/VPPC53923.2021.9699302

A comparative study on Optimal Control based torque vectoring systems

Asier Alonso
, Alberto Parra
, Asier Zubizarreta
, Inaki Sainz

ACS

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Electric vehicles (EVs) allow the implementation of multi-motor powertrains. These topologies allow to control the torque exerted in each active wheel of the vehicle, and thus, can naturally implement yaw moment control approaches such as Torque Vectoring, which improve vehicle stability and cornering response. Among the different control techniques proposed for this purpose, due to the recent improvement in computation power of embedded devices, optimal control based solutions have become one of the most promising alternatives. Hence, in this paper the performance of three different optimal based torque vectoring algorithms (Linear Quadratic Regulator, Linear Time-Variant Model-based Predictive Control and Nonlinear Model-based Predictive Control) are proposed, studied and comprehensively compared.

Original language	English
Title of host publication	2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665405287
DOIs	https://doi.org/10.1109/VPPC53923.2021.9699302
Publication status	Published - 2021
Externally published	Yes
Event	18th IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - Virtual, Gijon, Spain Duration: 25 Oct 2021 → 28 Oct 2021

Publication series

Name	2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS

Conference

Conference	18th IEEE Vehicle Power and Propulsion Conference, VPPC 2021
Country/Territory	Spain
City	Virtual, Gijon
Period	25/10/21 → 28/10/21

Keywords

Electric Vehicles (EVs)
Linear quadratic regulator (LQR)
Model predictive control (MPC)
Optimal control
Torque vectoring

Access to Document

10.1109/VPPC53923.2021.9699302

Cite this

Alonso, A., Parra, A., Zubizarreta, A., & Sainz, I. (2021). A comparative study on Optimal Control based torque vectoring systems. In 2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS (2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VPPC53923.2021.9699302

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title = "A comparative study on Optimal Control based torque vectoring systems",

abstract = "Electric vehicles (EVs) allow the implementation of multi-motor powertrains. These topologies allow to control the torque exerted in each active wheel of the vehicle, and thus, can naturally implement yaw moment control approaches such as Torque Vectoring, which improve vehicle stability and cornering response. Among the different control techniques proposed for this purpose, due to the recent improvement in computation power of embedded devices, optimal control based solutions have become one of the most promising alternatives. Hence, in this paper the performance of three different optimal based torque vectoring algorithms (Linear Quadratic Regulator, Linear Time-Variant Model-based Predictive Control and Nonlinear Model-based Predictive Control) are proposed, studied and comprehensively compared.",

keywords = "Electric Vehicles (EVs), Linear quadratic regulator (LQR), Model predictive control (MPC), Optimal control, Torque vectoring",

author = "Asier Alonso and Alberto Parra and Asier Zubizarreta and Inaki Sainz",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE Vehicle Power and Propulsion Conference, VPPC 2021 ; Conference date: 25-10-2021 Through 28-10-2021",

year = "2021",

doi = "10.1109/VPPC53923.2021.9699302",

language = "English",

series = "2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS",

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}

Alonso, A, Parra, A, Zubizarreta, A & Sainz, I 2021, A comparative study on Optimal Control based torque vectoring systems. in 2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS. 2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS, Institute of Electrical and Electronics Engineers Inc., 18th IEEE Vehicle Power and Propulsion Conference, VPPC 2021, Virtual, Gijon, Spain, 25/10/21. https://doi.org/10.1109/VPPC53923.2021.9699302

A comparative study on Optimal Control based torque vectoring systems. / Alonso, Asier; Parra, Alberto; Zubizarreta, Asier et al.
2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A comparative study on Optimal Control based torque vectoring systems

AU - Alonso, Asier

AU - Parra, Alberto

AU - Zubizarreta, Asier

AU - Sainz, Inaki

PY - 2021

Y1 - 2021

N2 - Electric vehicles (EVs) allow the implementation of multi-motor powertrains. These topologies allow to control the torque exerted in each active wheel of the vehicle, and thus, can naturally implement yaw moment control approaches such as Torque Vectoring, which improve vehicle stability and cornering response. Among the different control techniques proposed for this purpose, due to the recent improvement in computation power of embedded devices, optimal control based solutions have become one of the most promising alternatives. Hence, in this paper the performance of three different optimal based torque vectoring algorithms (Linear Quadratic Regulator, Linear Time-Variant Model-based Predictive Control and Nonlinear Model-based Predictive Control) are proposed, studied and comprehensively compared.

AB - Electric vehicles (EVs) allow the implementation of multi-motor powertrains. These topologies allow to control the torque exerted in each active wheel of the vehicle, and thus, can naturally implement yaw moment control approaches such as Torque Vectoring, which improve vehicle stability and cornering response. Among the different control techniques proposed for this purpose, due to the recent improvement in computation power of embedded devices, optimal control based solutions have become one of the most promising alternatives. Hence, in this paper the performance of three different optimal based torque vectoring algorithms (Linear Quadratic Regulator, Linear Time-Variant Model-based Predictive Control and Nonlinear Model-based Predictive Control) are proposed, studied and comprehensively compared.

KW - Electric Vehicles (EVs)

KW - Linear quadratic regulator (LQR)

KW - Model predictive control (MPC)

KW - Optimal control

KW - Torque vectoring

UR - https://www.scopus.com/pages/publications/85126212138

U2 - 10.1109/VPPC53923.2021.9699302

DO - 10.1109/VPPC53923.2021.9699302

M3 - Conference contribution

AN - SCOPUS:85126212138

T3 - 2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS

BT - 2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th IEEE Vehicle Power and Propulsion Conference, VPPC 2021

Y2 - 25 October 2021 through 28 October 2021

ER -

A comparative study on Optimal Control based torque vectoring systems

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