Detail publikace

Hybrid Model Predictive Control for Fully Electric Vehicle Thermal Management System Optimal Mode Selection

GLOS, J. ŠOLC, F. OTAVA, L. VÁCLAVEK, P.

Originální název

Hybrid Model Predictive Control for Fully Electric Vehicle Thermal Management System Optimal Mode Selection

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Vehicle thermal management systems of Fully Electric Vehicles bring increased demands on control algorithms to operate the vehicle efficiently. Especially, if there are multiple heat sources and sinks (cabin, batteries, electric drive, thermal energy storage, etc.), it is necessary to select the system operating mode (configuration of actuators), under which the system will operate efficiently with respecting defined constraints and references tracking. This paper brings a novel approach to the decision-making algorithm, which is based on the Hybrid Model Predictive Control and optimally solves the problem with regards to the defined objective function.

Klíčová slova

vehicle thermal management system; VTMS; model predictive control; MPC; hybrid model predictive control; HMPC; piecewise-affine; PWA; thermal energy storage; TES; heat pump; fully electric vehicle; FEV; vapor compression refrigeration system; VCRS; waste heat recovery; decision-making algorithm

Autoři

GLOS, J.; ŠOLC, F.; OTAVA, L.; VÁCLAVEK, P.

Vydáno

18. 10. 2020

Nakladatel

IEEE

Místo

New York

ISBN

978-1-7281-5414-5

Kniha

Proceedings of the IECON 2020 - The 46th Annual Conference of the IEEE Industrial Electronics Society

Strany od

2036

Strany do

2043

Strany počet

8

URL

Plný text v Digitální knihovně

BibTex

@inproceedings{BUT165335,
  author="Jan {Glos} and František {Šolc} and Lukáš {Otava} and Pavel {Václavek}",
  title="Hybrid Model Predictive Control for Fully Electric Vehicle Thermal Management System Optimal Mode Selection",
  booktitle="Proceedings of the IECON 2020 - The 46th Annual Conference of the IEEE Industrial Electronics Society",
  year="2020",
  pages="2036--2043",
  publisher="IEEE",
  address="New York",
  doi="10.1109/IECON43393.2020.9254286",
  isbn="978-1-7281-5414-5",
  url="https://ieeexplore.ieee.org/document/9254286"
}