Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim ® Validation

Riccardo Cespi, Renato Galluzzi, Ricardo A. Ramirez-Mendoza and Stefano Di Gennaro
Additional contact information
Riccardo Cespi: School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico
Renato Galluzzi: School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico
Ricardo A. Ramirez-Mendoza: School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico
Stefano Di Gennaro: Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, Via Vetoio, Loc. Coppito, 67100 L’Aquila, Italy

Mathematics, 2021, vol. 9, issue 23, 1-27

Abstract: This paper presents an active controller for electric vehicles in which active front steering and torque vectoring are control actions combined to improve the vehicle driving safety. The electric powertrain consists of four independent in–wheel electric motors situated on each corner. The control approach relies on an inverse optimal controller based on a neural network identifier of the vehicle plant. Moreover, to minimize the number of sensors needed for control purposes, the authors present a discrete–time reduced–order state observer for the estimation of vehicle lateral and roll dynamics. The use of a neural network identifier presents some interesting advantages. Notably, unlike standard strategies, the proposed approach avoids the use of tire lateral forces or Pacejka’s tire parameters. In fact, the neural identification provides an input–affine model in which these quantities are absorbed by neural synaptic weights adapted online by an extended Kalman filter. From a practical standpoint, this eliminates the need of additional sensors, model tuning, or estimation stages. In addition, the yaw angle command given by the controller is converted into electric motor torques in order to ensure safe driving conditions. The mathematical models used to describe the electric machines are able to reproduce the dynamic behavior of Elaphe M700 in–wheel electric motors. Finally, quality and performances of the proposed control strategy are discussed in simulation, using a CarSim ® full vehicle model running through a double–lane change maneuver.

Keywords: electric vehicles; in–wheel; neural network; inverse optimal control; extended Kalman filter; electric motors; CarSim ® (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/23/3120/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/23/3120/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:23:p:3120-:d:694473

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().