English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/166363
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:

Title

Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles

AuthorsLopez-Sanz, Jorge; Ocampo-Martinez, Carlos ; Alvarez-Florez, Jesus; Moreno-Eguilaz, Manuel; Ruiz-Mansilla, Rafael; Kalmus, Julian; Gräeber, Manuel; Lux, Gerhard
KeywordsThermal management
Plug-in hybrid electric vehicles (PHEV)
Li-ion battery cooling
Nonlinear model predictive control (NMPC)
Issue Date2016
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE Transactions on Vehicular Technology 66 (2016)
AbstractA nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input-Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi-domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.
Publisher version (URL)https://doi.org/10.1109/TVT.2016.2597242
URIhttp://hdl.handle.net/10261/166363
Identifiersdoi: 10.1109/TVT.2016.2597242
issn: 0018-9545
Appears in Collections:(IRII) Artículos
Files in This Item:
File Description SizeFormat 
NonlineaVehicle.pdf1,25 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.