Adaptive model predictive control with propulsion load estimation and prediction for all-electric ship energy management

Jun Hou, Jing Sun and Heath Hofmann

Energy, 2018, vol. 150, issue C, 877-889

Abstract: Electric ships experience large propulsion-load fluctuations on their drive shaft due to encountered waves and the rotational motion of the propeller, affecting the reliability of the shipboard power network and causing wear and tear. To address the load fluctuations, model predictive control has been explored as an effective solution. However, the load torque of the propulsion system, knowledge of which is essential for model predictive control, is difficult to measure and includes multi-frequency fluctuations. To deal with this issue, an adaptive model predictive control is developed so that the load torque estimation and prediction can be incorporated into model predictive control. In order to evaluate the effectiveness of the proposed adaptive model predictive control, an input observer with linear prediction is developed as an alternative approach to obtain the load estimation and prediction. Comparative studies are performed to illustrate the importance of the load torque estimation and prediction, and demonstrate the effectiveness of the proposed adaptive model predictive control in terms of improved efficiency, enhanced reliability and reduced wear and tear.

Keywords: Propulsion-load torque estimation and prediction; Adaptive model predictive control; All-electric ship; Hybrid energy storage; Energy management strategy (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218304171
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:150:y:2018:i:c:p:877-889

DOI: 10.1016/j.energy.2018.03.019

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().