Non-Linear Model Predictive Control of Cabin Temperature and Air Quality in Fully Electric Vehicles

被引：32

作者：

Glos, Jan ^{[1
]}

Otava, Lukas ^{[1
]}

Vaclavek, Pavel ^{[1
,2
]}

机构：

[1] Brno Univ Technol, CEITEC Cent European Inst Technol, Brno 61200, Czech Republic

[2] Brno Univ Technol, Fac Elect Engn & Commun, Brno 61200, Czech Republic

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2021年 / 70卷 / 02期

基金：

欧盟地平线“2020”;

关键词：

Heating systems; Resistance heating; Waste heat; Atmospheric modeling; Air quality; Heat pumps; Temperature control; Air quality control; battery electric vehicle; extended kalman filter; fully electric vehicle; non-linear model predictive control; temperature control; vehicle cabin model;

D O I：

10.1109/TVT.2021.3054170

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This article describes an application of Non-linear Model Predictive Control algorithms on energy efficient control of fully electric vehicle cabin temperature and air quality. Since fully electric vehicles can not utilize waste heat from a powertrain (or there is not enough waste heat) as ICE vehicles do, it is necessary to employ advanced control approaches (especially for cabin heating) due to the possible mileage lost by using energy from the batteries for cabin conditioning. The basic idea behind this is to avoid the heat losses caused by excessive air exchange and to ensure a satisfactory air quality in combination with a user defined temperature. The Non-linear Model Predictive control algorithms were successfully implemented into an Infineon AURIX Tricore microcontroller and tested within a Processor in the Loop simulation.

引用

页码：1216 / 1229

页数：14

共 41 条

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