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Explicit Nonlinear Model Predictive Control for Electric Vehicle Traction Control

2018/06/20 by Davide Tavernini, Mathias Metzler, Patrick Gruber +1
Engineering · #Electric and Hybrid Vehicle Technologies #Real-time simulation and control systems #Vehicle Dynamics and Control Systems

paper · pdf · doi:10.1109/tcst.2018.2837097

openalex publication_date 2018/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

This paper presents a traction control (TC) system for electric vehicles with in-wheel motors, based on explicit nonlinear model predictive control. The feedback law, available beforehand, is described in detail, together with its variation for different plant conditions. The explicit controller is implemented on a rapid control prototyping unit, which proves the real-time capability of the strategy, with computing times on the order of microseconds. These are significantly lower than the required time step for a TC application. Hence, the explicit model predictive controller can run at the same frequency as a simple TC system based on proportional integral (PI) technology. High-fidelity model simulations provide: 1) a performance comparison of the proposed explicit nonlinear model predictive controller (NMPC) with a benchmark PI-based traction controller with gain scheduling and anti-windup features, and 2) a performance comparison among two explicit and one implicit NMPCs based on different internal models, with and without consideration of transient tire behavior and load transfers. Experimental test results on an electric vehicle demonstrator are shown for one of the explicit NMPC formulations.

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