Optimization of Energy Consumption in Ship Propulsion Control under Severe Sea Conditions

With the further establishment of relevant regulations on ship emissions by countries worldwide and the IMO, and the increasing frequency of severe sea conditions in shipping routes, optimizing ship energy efficiency under high wind and wave conditions has become an important research direction. This study establishes a grey-box model for optimizing ships’ energy consumption under severe sea conditions, with wave heights above two meters and a Beaufort scale score above five, based on the principle of ship–engine–propeller matching and a non-dominated sorting optimization algorithm. Using historical navigation data from a case ship under severe sea conditions, a white-box model and a black-box model for ship fuel consumption were established. These models were combined to create a grey-box model for ship fuel consumption. The K-Medoids clustering algorithm was used to cluster severe sea conditions. The optimization variables were the main engine’s speed, with the fuel consumption per nautical mile and the ship’s speed being used as optimization objectives. The non-dominated sorting genetic algorithm was optimized for each sea condition, resulting in the best speed for each sea state. The results indicate that the model developed in this paper reduced the main engine’s fuel consumption per nautical mile by 21.9% and increased the speed by 16.7% under the most severe sea conditions. Therefore, the proposed model effectively optimizes ship energy efficiency and reduces navigation time under severe sea conditions, providing an effective solution for operations in actual severe sea conditions.