Abstract
Hybrid energy storage systems have been widely used in transportation, microgrid and renewable energy applications to improve system efficiency and enhance reliability. However, parameter uncertainty can significantly affect system performance. In order to address this issue, an adaptive model predictive control is developed in this paper. Online parameter identification is used to mitigate parameter uncertainty, and model predictive control is used to optimally split power, deal with constraints, and achieve desired dynamic responses. A sensitivity analysis is conducted to identify major impact factors. In order to validate the proposed method, both simulation and experiments are performed to show the effectiveness of the proposed adaptive model predictive control. Compared to the model predictive control without online parameter identification, the power loss reduction can be as high as 15% in the experiments. This study focuses on all-electric ship energy management to mitigate load fluctuations and improve system efficiency and reliability. The proposed method could also be used in other applications.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
