Embedded Real-Time Nonlinear Model Predictive Control for the Thermal Torque Derating of an Electric Vehicle

被引：5

作者：

Winkler, Alexander ^{[1
]}

Frey, Jonathan ^{[2
]}

Fahrbach, Timm ^{[1
]}

Frison, Gianluca ^{[2
]}

Scheer, Rene ^{[1
]}

Diehl, Moritz ^{[2
]}

Andert, Jakob ^{[1
]}

机构：

[1] Rhein Westfal TH Aachen, Teaching & Res Area Mechatron Mobile Prop, Forckenbeckstr 4, D-52074 Aachen, Germany

[2] Albert Ludwigs Univ Freiburg, Dept Microsyst Engn, Georges Kohler Allee 103, D-79110 Freiburg, Germany

来源：

IFAC PAPERSONLINE | 2021年 / 54卷 / 06期

关键词：

nonlinear model predictive control; real-time control; automotive control; optimal control; permanent magnet motors; embedded systems; temperature control;

D O I：

10.1016/j.ifacol.2021.08.570

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a real-time capable nonlinear model predictive control (NMPC) strategy to effectively control the driving performance of an electric vehicle (EV) while optimizing thermal utilization. The prediction model is based on an experimentally validated two-node lumped parameter thermal network (LPTN) and one-dimensional driving dynamics. An efficient solver for the trajectory tracking problem is exported using acados and deployed on a dSPACE SCALEXIO embedded system. The lap time of a high-load driving cycle compared to a state-of-the-art derating strategy improved by 2.56% with an energy consumption reduction of 2.43% while respecting the temperature constraints of the electric drive. Copyright (C) 2021 The Authors.

引用

页码：359 / 364

页数：6

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