TR2021-133

A Predictive Controller for Drivability and Comfort in Multi-Motor Electric Vehicles

    •  Chen, D., Danielson, C., Di Cairano, S., "A Predictive Controller for Drivability and Comfort in Multi-Motor Electric Vehicles", IFAC Modeling, Estimation and Control Conference (MECC), DOI: 10.1016/​j.ifacol.2021.11.245, October 2021, pp. 650-656.
      BibTeX TR2021-133 PDF
      • @inproceedings{Chen2021oct,
      • author = {Chen, Di and Danielson, Claus and Di Cairano, Stefano},
      • title = {A Predictive Controller for Drivability and Comfort in Multi-Motor Electric Vehicles},
      • booktitle = {IFAC Modeling, Estimation and Control Conference (MECC)},
      • year = 2021,
      • pages = {650--656},
      • month = oct,
      • publisher = {Elsevier},
      • doi = {10.1016/j.ifacol.2021.11.245},
      • url = {https://www.merl.com/publications/TR2021-133}
      • }
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  • Research Areas:

    Control, Dynamical Systems

Abstract:

This paper presents a robust rate-based model predictive control (rate-based MPC) for controlling electric vehicle (EV) with independently actuated wheels and anti-squat/lift/dive suspensions. We present steps by which we arrive at a controller with good tracking performance, the capability to improve passenger comfort by reducing the lift, pitch, and roll motion of the vehicle chassis, and the ability to modify the reference to maintain vehicle lateral stability.
CarSim simulation results are presented that demonstrate the ability of rate-based MPC to achieve good longitudinal acceleration and yaw rate tracking while reducing the suspension motions, despite the discrepancy between the high-fidelity CarSim model and the control-oriented model.