Simplified rule-based strategy control for electric propulsion system
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초록
The current global trends regarding tightened environmental regulations on emissions from ships have motivated the devel-opment and usage of electric propulsion systems as alternatives to meet the regulatory changes. Electric propulsion systems have the advantage of being able to utilize various power sources, thereby enabling a grid connection of eco-friendly power sources. However, because the operating characteristics vary depending on the type of power source, the design of an energy management system suitable for the ship’s operating environment is necessary to facilitate optimum control of the energy system. Amongst the various control strategies available, state machines and rule-based controls have been employed extensively in hybrid systems. In addition, the rules have been incorporated into the neural network algorithm to enable the operation of complex systems or under unpredictable circum-stances. In this study, the conventional rule-based strategy was simplified, and a neural network was trained with a revised control strategy. The operability of the hybrid system was examined through MATLAB/Simulink simulations. The findings of this study are expected to be of use as an energy management system for batteries and generators, as well as various other eco-friendly power gen-eration systems.
Abstract
The current global trends regarding tightened environmental regulations on emissions from ships have motivated the devel-opment and usage of electric propulsion systems as alternatives to meet the regulatory changes. Electric propulsion systems have the advantage of being able to utilize various power sources, thereby enabling a grid connection of eco-friendly power sources. However, because the operating characteristics vary depending on the type of power source, the design of an energy management system suitable for the ship’s operating environment is necessary to facilitate optimum control of the energy system. Amongst the various control strategies available, state machines and rule-based controls have been employed extensively in hybrid systems. In addition, the rules have been incorporated into the neural network algorithm to enable the operation of complex systems or under unpredictable circum-stances. In this study, the conventional rule-based strategy was simplified, and a neural network was trained with a revised control strategy. The operability of the hybrid system was examined through MATLAB/Simulink simulations. The findings of this study are expected to be of use as an energy management system for batteries and generators, as well as various other eco-friendly power gen-eration systems.
참고문헌 (9)
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