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http://dx.doi.org/10.6109/jkiice.2020.24.9.1202

Battery-loaded power management algorithm of electric propulsion ship based on power load and state learning model  

Oh, Ji-hyun (Marine Engineering, Korea Maritime and Ocean University)
Oh, Jin-seok (Marine Engineering, Korea Maritime and Ocean University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.9, 2020 , pp. 1202-1208 More about this Journal
Abstract
In line with the current era of the 4th Industrial Revolution, it is necessary to prepare for the future by integrating AI elements in the ship sector. In addition, it is necessary to respond to this in the field of power management for the appearance of autonomous ships. In this study, we propose a battery-linked electric propulsion system (BLEPS) algorithm using machine learning's DNN. For the experiment, we learned the pattern of ship power consumption for each operation mode based on the ship data through LabView and derived the battery status through Python to check the flexibility of the generator and battery interlocking. As a result of the experiment, the low load operation of the generator was reduced through charging and discharging of the battery, and economic efficiency and reliability were confirmed by reducing the fuel consumption of 1% of LNG.
Keywords
Electric propulsion ship; machine learning; generator optimal operation; battery;
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
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