Amir Taghavipour (Author), Ali Alipour (Author)

Abstract

One of the most important challenges facing automotive engineers is reducing vehicle fuel consumption and improving the drivability index. Modern hybrid electric powertrains play an important role in reducing fuel consumption. Continuously variable transmission (CVT) is an automatic transmission that can change the gear ratio seamlessly using a belt and pulleys. CVT performs with infinite gear ratios. Controlling and determining the optimal gear ratio, especially in a complex hybrid powertrain, is a major challenge. Therefore, a multi-parametric model predictive controller with real-time implementation capability is proposed that can handle the energy management task concurrently with gear shifting strategy in a parallel pre-transmission hybrid electric vehicle. The proposed controller hardware-in-the-loop (HIL) validation procedure on the high-fidelity Autonomie model shows significant improvement in fuel economy while maintaining drivability in three different driving schedules. HIL evaluation guarantees the real-time capability and the proposed controller readiness to be implemented to real-world control hardware.

Keywords

hybrid electric vehicle;continuously variable transmission;Autonomie;energy management system;explicit model predictive control;hardware in the loop;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FS - Faculty of Mechanical Engineering
UDC: 629.3:621.83.069.2
COBISS: 69568771 Link will open in a new window
ISSN: 0039-2480
Views: 267
Downloads: 70
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Other data

Secondary language: Slovenian
Secondary title: HIL validacija novega realnočasovnega sistema upravljanja z energijo za HEV z brezstopenjskim menjalnikom
Secondary keywords: hibridno električno vozilo;CVT;Autonomie;sistem upravljanja z energijo;eksplicitno modelno prediktivno vodenje;strojna oprema v zanki;
Type (COBISS): Article
Pages: str.142-152
Volume: ǂVol. ǂ67
Issue: ǂno. ǂ4
Chronology: Apr. 2021
DOI: 10.5545/sv-jme.2020.7017
ID: 13112535