A novel hybrid energy management strategy of a diesel-electric hybrid ship based on dynamic programing and model predictive control
The energy-saving characteristics of diesel-electric series hybrid ships largely depend on their energy management strategy. In this paper, a strategy that combines dynamic programing and model predictive control (DP-MPC) is proposed to solve the energy management problems of diesel-electric hybrid ships. The DP-MPC strategy has considered some typical working conditions of a ship, and the corresponding influence of white noise disturbance on the control strategy was studied. The simulation results show that the DP-MPC strategy has an excellent anti-interference capability. The control performance of the DP-MPC strategy is then further analyzed and compared with the rule-based logic threshold control strategy. The simulation results show that the proposed DP-MPC strategy can save 2.5% of the fuel consumption and has a better anti-interference capability than the rule-based control strategy.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14750902
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Supplemental Notes:
- © IMechE 2022.
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Authors:
- Yuan, Yupeng
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0000-0001-9474-0605
- Chen, Mingshuang
- Wang, Jixiang
- Yu, Wanneng
- Shen, Boyang
- Publication Date: 2022-8
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 644-657
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Serial:
- Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment
- Volume: 236
- Issue Number: 3
- Publisher: Sage Publications, Incorporated
- ISSN: 1475-0902
- EISSN: 2041-3084
- Serial URL: http://pim.sagepub.com/
Subject/Index Terms
- TRT Terms: Dynamic programming; Energy consumption; Hybrid vehicles; Interference; Ships
- Identifier Terms: Model Predictive Control
- Subject Areas: Data and Information Technology; Marine Transportation; Vehicles and Equipment;
Filing Info
- Accession Number: 01856978
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 31 2022 9:20AM