To protect the marine environment, the International Maritime Organization is strengthening regulations on ship emissions such as sulfur oxides, nitrogen oxides, and carbon dioxide. In addition, according to the 4th industrial revolution, research on the application of ICT(Information and Communications Technologies) and artificial intelligence technology such as Big Data and IoT are proceeding in shipbuilding and marine fields; therefore, an electric propulsion system that is easy to monitor and control with its base technology is required. Consequently, the future development of ship technologies that enhance energy efficiency while satisfying marine regulations on emission is expected.
The purpose of this study is to improve the performance of the load control system (LCSI) algorithm applied to an electric propulsion system. An LCS reduces energy by increasing the power generation efficiency of generators through load sharing in generators and batteries. The LCS has been applied to the power systems of mechanical propulsion systems. As ship propulsion systems move from mechanical to electrical, the propulsion load is integrated into the power load. To apply LCS to the electric propulsion system, additional load analysis and improved LCS algorithms are required considering the load condition.
In this study, an LCS for the electric propulsion system was applied to a container ship. Hitherto, no electric propulsion system has been applied to container ships; as such, a virtual electric propulsion system was designed and applied to the container ship. In the power system design process, the generator and battery capacity were designed to operate the generator at the highest efficiency power range through load analysis.
In the LCS algorithm improvement process, the total power load (including the propulsion and auxiliary loads) was analyzed through load analysis. Through the load analysis using a self-organizing map, the load data were classified into several clusters and the characteristics of the load could be extracted. An algorithm that divides modes by the load characteristics and control them according to the load characteristics was added. The ship load condition data were collected and used to calculate the energy coefficients, energy flexibility, and energy efficiency. Energy flexibility refers to the ability of the generator–battery system to supply power to load fluctuations. Energy efficiency refers to the ability of the generator to produce power at the highest efficiency power range. Energy flexibility and energy efficiency were applied to the load sharing control of the LCS between the generator and battery. Finally, an improved LCS algorithm was evaluated by simulation using the virtual electric propulsion system model.