Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim<sup>®</sup> Validation
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 ap...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/9/23/3120 |
| _version_ | 1797507451244773376 |
|---|---|
| author | Riccardo Cespi Renato Galluzzi Ricardo A. Ramirez-Mendoza Stefano Di Gennaro |
| author_facet | Riccardo Cespi Renato Galluzzi Ricardo A. Ramirez-Mendoza Stefano Di Gennaro |
| author_sort | Riccardo Cespi |
| collection | DOAJ |
| description | 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<sup>®</sup> full vehicle model running through a double–lane change maneuver. |
| first_indexed | 2024-03-10T04:48:42Z |
| format | Article |
| id | doaj.art-b9cfd222c3db420aa7704ad1caa416b0 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T04:48:42Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-b9cfd222c3db420aa7704ad1caa416b02023-11-23T02:46:22ZengMDPI AGMathematics2227-73902021-12-01923312010.3390/math9233120Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim<sup>®</sup> ValidationRiccardo Cespi0Renato Galluzzi1Ricardo A. Ramirez-Mendoza2Stefano Di Gennaro3School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, MexicoDepartment of Information Engineering, Computer Science and Mathematics, University of L’Aquila, Via Vetoio, Loc. Coppito, 67100 L’Aquila, ItalyThis 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<sup>®</sup> full vehicle model running through a double–lane change maneuver.https://www.mdpi.com/2227-7390/9/23/3120electric vehiclesin–wheelneural networkinverse optimal controlextended Kalman filterelectric motors |
| spellingShingle | Riccardo Cespi Renato Galluzzi Ricardo A. Ramirez-Mendoza Stefano Di Gennaro Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim<sup>®</sup> Validation Mathematics electric vehicles in–wheel neural network inverse optimal control extended Kalman filter electric motors |
| title | Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim<sup>®</sup> Validation |
| title_full | Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim<sup>®</sup> Validation |
| title_fullStr | Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim<sup>®</sup> Validation |
| title_full_unstemmed | Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim<sup>®</sup> Validation |
| title_short | Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim<sup>®</sup> Validation |
| title_sort | artificial intelligence for stability control of actuated in wheel electric vehicles with carsim sup r sup validation |
| topic | electric vehicles in–wheel neural network inverse optimal control extended Kalman filter electric motors |
| url | https://www.mdpi.com/2227-7390/9/23/3120 |
| work_keys_str_mv | AT riccardocespi artificialintelligenceforstabilitycontrolofactuatedinwheelelectricvehicleswithcarsimsupsupvalidation AT renatogalluzzi artificialintelligenceforstabilitycontrolofactuatedinwheelelectricvehicleswithcarsimsupsupvalidation AT ricardoaramirezmendoza artificialintelligenceforstabilitycontrolofactuatedinwheelelectricvehicleswithcarsimsupsupvalidation AT stefanodigennaro artificialintelligenceforstabilitycontrolofactuatedinwheelelectricvehicleswithcarsimsupsupvalidation |