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http://dx.doi.org/10.25673/35095
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DC Field | Value | Language |
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dc.contributor.author | Robuschi, Nicolò | - |
dc.contributor.author | Zeile, Clemens | - |
dc.contributor.author | Sager, Sebastian | - |
dc.contributor.author | Braghin, Francesco | - |
dc.date.accessioned | 2020-11-20T09:52:07Z | - |
dc.date.available | 2020-11-20T09:52:07Z | - |
dc.date.issued | 2020 | - |
dc.date.submitted | 2021 | - |
dc.identifier.uri | https://opendata.uni-halle.de//handle/1981185920/35298 | - |
dc.identifier.uri | http://dx.doi.org/10.25673/35095 | - |
dc.description.abstract | This study considers the problem of computing a non-causal minimum-fuel energy management strategy for a hybrid electric vehicle on a given driving cycle. Specifically, we address the multiphase mixed-integer nonlinear optimal control problem that arises when the optimal gear choice, torque split and engine on/off controls are sought in off-line evaluations. We propose an efficient model by introducing vanishing constraints and a phase specific right-hand side function that accounts for the different powertrain operating modes. The gearbox and driveability requirements translate into combinatorial constraints. These constraints have not been included in previous research; however, they are part of the algorithmic framework for this investigation. We devise a tailored algorithm to solve this problem by extending the combinatorial integral approximation (CIA) technique that breaks down the original mixed-integer nonlinear program into a sequence of nonlinear programs and mixed-integer linear programs, followed by a discussion of its approximation error. Finally, numerical results illustrate the proposed algorithm in terms of solution quality and run time. | eng |
dc.format.extent | 1 Online-Ressource (10 Seiten) | - |
dc.language.iso | eng | - |
dc.publisher | Elsevier, Amsterdam | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Hybrid electric vehicles | eng |
dc.subject | Hybrid powertrains | eng |
dc.subject | Energy management | eng |
dc.subject | Nonlinear programming | eng |
dc.subject | Mixed-integer nonlinear optimal control | eng |
dc.subject.ddc | 519.6 | - |
dc.title | Multiphase mixed-integer nonlinear optimal control of hybrid electric vehicles | eng |
dc.type | Article | - |
dc.identifier.urn | urn:nbn:de:gbv:ma9:1-1981185920-352983 | - |
dc.relation.references | https://www.journals.elsevier.com/automatica | - |
local.versionType | publishedVersion | - |
local.bibliographicCitation.journaltitle | Automatica | - |
local.bibliographicCitation.volume | 123 | - |
local.bibliographicCitation.issue | 2021 | - |
local.bibliographicCitation.pagestart | 1 | - |
local.bibliographicCitation.pageend | 10 | - |
local.bibliographicCitation.publishername | Elsevier | - |
local.bibliographicCitation.publisherplace | Amsterdam | - |
local.bibliographicCitation.doi | 10.1016/j.automatica.2020.109325 | - |
local.openaccess | true | - |
dc.identifier.ppn | 1740222075 | - |
local.publication.country | XA-NL | - |
cbs.sru.importDate | 2020-11-20T09:46:46Z | - |
local.bibliographicCitation | Sonderdruck aus Automatica | - |
local.accessrights.dnb | free | - |
Appears in Collections: | Fakultät für Mathematik (OA) |
Files in This Item:
File | Description | Size | Format | |
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Sager_et al._Automatica_2020.pdf | Zweitveröffentlichung | 1.5 MB | Adobe PDF | View/Open |