Details
| Original language | English |
|---|---|
| Article number | 104534 |
| Journal | Control Engineering Practice |
| Volume | 102 |
| Early online date | 8 Jul 2020 |
| Publication status | Published - Sept 2020 |
Abstract
In this work, the problem of online energy management of hybrid electric vehicles is addressed. A least costly objective function accounting for battery energy consumption and aging, and for the auxiliary power unit fuel consumption and noise emissions is considered. In this scenario, all the cost terms are expressed as monetary variables. This allows to assess the economic effectiveness of the proposed hybrid powertrain solution. Therefore, the online energy management policy is computed relying on the economic model predictive control framework. Some dissipativity properties for steady-state and periodic operation of the system under investigation are proved. Therefore, some results for close to optimum convergence of the economic model predictive control are provided. An electric bus case-study is illustrated in detail to show the performance of the proposed online management strategy.
Keywords
- Differential inclusions, Dissipativity, Economic model predictive control, Energy management, Hybrid electric vehicle
ASJC Scopus subject areas
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science Applications
Sustainable Development Goals
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In: Control Engineering Practice, Vol. 102, 104534, 09.2020.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Economic MPC for online least costly energy management of hybrid electric vehicles
AU - Pozzato, Gabriele
AU - Müller, Matthias
AU - Formentin, Simone
AU - Savaresi, Sergio M.
N1 - Funding information: This work was partially sponsored by Steyr Motors GmbH, Germany and the Linz Center of Mechatronics (LCM), Austria .
PY - 2020/9
Y1 - 2020/9
N2 - In this work, the problem of online energy management of hybrid electric vehicles is addressed. A least costly objective function accounting for battery energy consumption and aging, and for the auxiliary power unit fuel consumption and noise emissions is considered. In this scenario, all the cost terms are expressed as monetary variables. This allows to assess the economic effectiveness of the proposed hybrid powertrain solution. Therefore, the online energy management policy is computed relying on the economic model predictive control framework. Some dissipativity properties for steady-state and periodic operation of the system under investigation are proved. Therefore, some results for close to optimum convergence of the economic model predictive control are provided. An electric bus case-study is illustrated in detail to show the performance of the proposed online management strategy.
AB - In this work, the problem of online energy management of hybrid electric vehicles is addressed. A least costly objective function accounting for battery energy consumption and aging, and for the auxiliary power unit fuel consumption and noise emissions is considered. In this scenario, all the cost terms are expressed as monetary variables. This allows to assess the economic effectiveness of the proposed hybrid powertrain solution. Therefore, the online energy management policy is computed relying on the economic model predictive control framework. Some dissipativity properties for steady-state and periodic operation of the system under investigation are proved. Therefore, some results for close to optimum convergence of the economic model predictive control are provided. An electric bus case-study is illustrated in detail to show the performance of the proposed online management strategy.
KW - Differential inclusions
KW - Dissipativity
KW - Economic model predictive control
KW - Energy management
KW - Hybrid electric vehicle
UR - http://www.scopus.com/inward/record.url?scp=85087504239&partnerID=8YFLogxK
U2 - 10.1016/j.conengprac.2020.104534
DO - 10.1016/j.conengprac.2020.104534
M3 - Article
VL - 102
JO - Control Engineering Practice
JF - Control Engineering Practice
SN - 0967-0661
M1 - 104534
ER -