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.
Highlights
The automotive industry is facing an ongoing evolution towards electrification and inclusion of so–called smart features
The active front steering (AFS) is directly given as control feedback to the vehicle plant model, whereas the torque vectoring (TV) is split into two different components, namely, the electric motor torques to be given to the left and right side of the powertrain, τl,k and τr,k, respectively
The use of an in–wheel powertrain implies a direct equivalence between the speeds of the electric motor and the vehicle wheel
Summary
The automotive industry is facing an ongoing evolution towards electrification and inclusion of so–called smart features. Torque optimization strategies with focus on regenerative braking and energy efficiency have been explored [7,8] In their recent systematic review, Mazzilli et al [4] found that most of the ICC implementations target the enhancement of lateral vehicle dynamics through improved utilization of the tire–road friction potential. Since the contact between the tire and the ground plays a fundamental role in propulsion and vehicle stability, previous efforts have focused on the estimation of the tire side–slip angle [9,10] Many of these works assume the full knowledge of lateral tire forces, which, from a practical standpoint requires dedicated sensors and/or estimation strategies.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have