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http://dx.doi.org/10.25673/35095| Titel: | Multiphase mixed-integer nonlinear optimal control of hybrid electric vehicles |
| Autor(en): | Robuschi, Nicolò Zeile, Clemens Sager, Sebastian Braghin, Francesco |
| Erscheinungsdatum: | 2020 |
| Umfang: | 1 Online-Ressource (10 Seiten) |
| Art: | Artikel |
| Sprache: | Englisch |
| Herausgeber: | Elsevier, Amsterdam |
| URN: | urn:nbn:de:gbv:ma9:1-1981185920-352983 |
| Schlagwörter: | Hybrid electric vehicles Hybrid powertrains Energy management Nonlinear programming Mixed-integer nonlinear optimal control |
| Zusammenfassung: | 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. |
| URI: | https://opendata.uni-halle.de//handle/1981185920/35298 http://dx.doi.org/10.25673/35095 |
| Open-Access: | Open-Access-Publikation |
| Nutzungslizenz: | (CC BY 4.0) Creative Commons Namensnennung 4.0 International |
| Journal Titel: | Automatica |
| Verlag: | Elsevier |
| Verlagsort: | Amsterdam |
| Band: | 123 |
| Heft: | 2021 |
| Originalveröffentlichung: | 10.1016/j.automatica.2020.109325 |
| Seitenanfang: | 1 |
| Seitenende: | 10 |
| Enthalten in den Sammlungen: | Fakultät für Mathematik (OA) |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| Sager_et al._Automatica_2020.pdf | Zweitveröffentlichung | 1.5 MB | Adobe PDF | ![]() Öffnen/Anzeigen |
Open-Access-Publikation
