Journal article
Energy management in plug-in hybrid electric vehicles: Convex optimization algorithms for model predictive control
- Abstract:
- This article details an investigation into the computational performance of algorithms used for solving a convex formulation of the optimization problem associated with model predictive control for energy management in hybrid electric vehicles with nonlinear losses. A projected interior-point method is proposed, where the size and complexity of the Newton step matrix inversion is reduced by applying inequality constraints on the control input as a projection, and its properties are demonstrated through simulation in comparison with an alternating direction method of multipliers (ADMM) algorithm and a general purpose convex optimization software CVX. It is found that the ADMM algorithm has favorable properties when a solution with modest accuracy is required, whereas the projected interior-point method is favorable when high accuracy is required, and that both are significantly faster than CVX.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
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(Preview, Accepted manuscript, pdf, 1.5MB, Terms of use)
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- Publisher copy:
- 10.1109/TCST.2019.2933793
Authors
- Publisher:
- Institute of Electrical and Electronics Engineers
- Journal:
- IEEE Transactions on Control Systems Technology More from this journal
- Volume:
- 28
- Issue:
- 6
- Pages:
- 2191-2203
- Publication date:
- 2019-08-27
- Acceptance date:
- 2019-07-14
- DOI:
- EISSN:
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1558-0865
- ISSN:
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1063-6536
- Language:
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English
- Keywords:
- Pubs id:
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pubs:1038477
- UUID:
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uuid:75ab2d8f-c62f-44e5-b072-fc6389b0b2c3
- Local pid:
-
pubs:1038477
- Source identifiers:
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1038477
- Deposit date:
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2019-08-05
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2019
- Rights statement:
- © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/TCST.2019.2933793
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