A Mid-fidelity Model in the Loop Feasibility Study for Implementation of Regenerative Antilock Braking System in Electric Vehicles

Nastaran Ghanami, Kamyar Nikzadfar, Hamid Reza Mohammadi Daniali

Research output: Contribution to journalArticlepeer-review

Abstract

The tailpipe zero-emission legislation has pushed the automotive industry toward more electrification. Regenerative braking is the capability of electric machines to provide brake torque. So far, the regenerative braking feature is primarily considered due to its effect on energy efficiency. However, using individual e-machines for each wheel makes it possible to apply the antilock braking function due to the fast torque-tracking characteristics of permanent magnet synchronous motors (PMSM). Due to its considerable cost reduction, in this article, a feasibility study is carried out to investigate if the ABS function can be done purely through regenerative braking using a mid-fidelity model-based approach. An uni-tire model of the vehicle with a surface-mount PMSM (SPMSM) model is used to verify the idea. The proposed ABS control system has a hierarchical structure containing a high-level longitudinal slip controller and a low-level SPMSM torque controller. Given the uncertainties of the tire–road dynamics, a sliding mode control method is designed and employed as a high-level slip controller. Also, a PID vector control method is used to keep the SPMSM braking torque at the optimal value requested by the high-level controller. Moreover, in order to estimate the tire longitudinal slip and vehicle velocity, an extended Kalman filter (EKF) is developed that estimates both parameters at the same time. The results show that the proposed hierarchical control and estimators can keep the tire longitudinal slip at the optimal value and prevent the wheel from locking in a variety of road conditions.</div>
Original languageEnglish
Article number10-07-03-0022
Number of pages24
JournalSAE International Journal of Vehicle Dynamics, Stability, and NVH
Volume7
Issue number3
DOIs
Publication statusE-pub ahead of print - 29 Jul 2023

Keywords

  • Automotive Engineering
  • Computational Mechanics
  • Control and Optimization
  • Mechanical Engineering

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