An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control
This paper addresses the energy management problem of hybrid ships by proposing an event-triggered model predictive control (ET-MPC) method. The novelty in this work lies in the establishment of an event-triggered mechanism and a state prediction model for energy management of hybrid ships. First, t...
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Elsevier
2024-11-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524005350 |
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| author | Diju Gao Long Chen Yide Wang |
| author_facet | Diju Gao Long Chen Yide Wang |
| author_sort | Diju Gao |
| collection | DOAJ |
| description | This paper addresses the energy management problem of hybrid ships by proposing an event-triggered model predictive control (ET-MPC) method. The novelty in this work lies in the establishment of an event-triggered mechanism and a state prediction model for energy management of hybrid ships. First, torque models of the internal combustion engine (ICE) and electric machine (EM) are developed using a data-driven approach, followed by the construction of fuel consumption and carbon emission models. Second, an event-triggered mechanism, dependent on state prediction error, is introduced and updated at each time step based on the system’s current state. Additionally, a cubature Kalman filter (CKF) is employed to estimate and correct the state prediction error, minimizing inaccuracies. A trade-off coefficient is incorporated to optimize the balance between fuel consumption and carbon emissions. The ET-MPC method results in a 0.68% difference in fuel consumption and 3.43% increase emissions compared to the traditional MPC method. However, ET-MPC significantly reduces computational overhead by 56.66. The ET-MPC method effectively allocates the ship’s energy according to the varying trade-off coefficient, achieving optimal energy management under different constraints. |
| format | Article |
| id | doaj-art-aaca63c0e0d54acd8e3c19c202e2e03b |
| institution | OA Journals |
| issn | 0142-0615 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-aaca63c0e0d54acd8e3c19c202e2e03b2025-08-20T01:47:59ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152024-11-0116211031210.1016/j.ijepes.2024.110312An energy trade-off management strategy for hybrid ships based on event-triggered model predictive controlDiju Gao0Long Chen1Yide Wang2Key Laboratory of Transport Industry of Marine Technology and Control Engineering, Shanghai Maritime University, Shanghai 201306, China; Corresponding author.University of Science and Technology of China, Hefei 230026, China; Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaInstitut d’Electronique et des Technologies du numéRique, UMR CNRS 6164, Nantes Université, F-44000 Nantes, FranceThis paper addresses the energy management problem of hybrid ships by proposing an event-triggered model predictive control (ET-MPC) method. The novelty in this work lies in the establishment of an event-triggered mechanism and a state prediction model for energy management of hybrid ships. First, torque models of the internal combustion engine (ICE) and electric machine (EM) are developed using a data-driven approach, followed by the construction of fuel consumption and carbon emission models. Second, an event-triggered mechanism, dependent on state prediction error, is introduced and updated at each time step based on the system’s current state. Additionally, a cubature Kalman filter (CKF) is employed to estimate and correct the state prediction error, minimizing inaccuracies. A trade-off coefficient is incorporated to optimize the balance between fuel consumption and carbon emissions. The ET-MPC method results in a 0.68% difference in fuel consumption and 3.43% increase emissions compared to the traditional MPC method. However, ET-MPC significantly reduces computational overhead by 56.66. The ET-MPC method effectively allocates the ship’s energy according to the varying trade-off coefficient, achieving optimal energy management under different constraints.http://www.sciencedirect.com/science/article/pii/S0142061524005350Event-triggeredModel predictive controlEnergy managementCubature Kalman filter |
| spellingShingle | Diju Gao Long Chen Yide Wang An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control International Journal of Electrical Power & Energy Systems Event-triggered Model predictive control Energy management Cubature Kalman filter |
| title | An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control |
| title_full | An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control |
| title_fullStr | An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control |
| title_full_unstemmed | An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control |
| title_short | An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control |
| title_sort | energy trade off management strategy for hybrid ships based on event triggered model predictive control |
| topic | Event-triggered Model predictive control Energy management Cubature Kalman filter |
| url | http://www.sciencedirect.com/science/article/pii/S0142061524005350 |
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