Please use this identifier to cite or link to this item:
http://dx.doi.org/10.25673/35095
Title: | Multiphase mixed-integer nonlinear optimal control of hybrid electric vehicles |
Author(s): | Robuschi, Nicolò Zeile, Clemens Sager, Sebastian Braghin, Francesco |
Issue Date: | 2020 |
Extent: | 1 Online-Ressource (10 Seiten) |
Type: | Article |
Language: | English |
Publisher: | Elsevier, Amsterdam |
URN: | urn:nbn:de:gbv:ma9:1-1981185920-352983 |
Subjects: | Hybrid electric vehicles Hybrid powertrains Energy management Nonlinear programming Mixed-integer nonlinear optimal control |
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. |
URI: | https://opendata.uni-halle.de//handle/1981185920/35298 http://dx.doi.org/10.25673/35095 |
Open Access: | Open access publication |
License: | (CC BY 4.0) Creative Commons Attribution 4.0 |
Journal Title: | Automatica |
Publisher: | Elsevier |
Publisher Place: | Amsterdam |
Volume: | 123 |
Issue: | 2021 |
Original Publication: | 10.1016/j.automatica.2020.109325 |
Page Start: | 1 |
Page End: | 10 |
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 |