Wing-diesel engine-powered hybrid ships can effectively reduce fuel consumption and CO₂ emissions by using wind energy as the auxiliary driving power. The energy optimization management of the hybrid system can further improve the ship's energy efficiency. To achieve this purpose, it is significant to establish an effective energy consumption model for the energy optimization management of the hybrid system. Therefore, an energy consumption model is established based on the energy conversion analysis of the hybrid power system in this paper. This model can effectively describe the energy consumption of the hybrid ship under different navigational environmental conditions. Then, a joint optimization method of the wing attack angle and of the sailing speed for the hybrid ship is proposed by adopting a swarm intelligence optimization algorithm, in order to reduce energy consumption and CO₂ emissions of the hybrid ship under different navigational environmental conditions. Finally, the energy consumption optimization potentials by adopting the hybrid power system and the proposed joint optimization method are analyzed. The results show that the energy consumption and CO₂ emissions along a typical route can be reduced by about 4.5%. This study provides an important basis for future practical operations of wing-diesel engine-powered hybrid ships.

It is of significant importance to optimize the energy consumption of ships in order to improve economy and reduce CO₂ emissions. However, the energy use of ships is affected by a series of navigational environmental parameters, which have certain spatial and temporal differences and variability. Therefore, the dynamic collaborative optimization method of sailing route and speed, which fully considers the spatial and temporal distribution characteristics of those factors, is of great importance. In this paper, the spatial and temporal distribution characteristics of the environmental factors and their related ship energy consumption profiles are first analyzed. Subsequently, a ship energy consumption model considering various environmental factors is established to realize the prediction of energy use of ships within the navigation region. Then, a novel dynamic collaborative optimization algorithm, which adopts the Model Predictive Control (MPC) strategy and swarm intelligence algorithm, is proposed, to further improve the ship's energy consumption optimization. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method. The results show that the newly developed dynamic collaborative optimization method, which fully considers the continuously time-varying characteristics of environmental and operational parameters, could effectively reduce the energy consumption in comparison to the original operational mode. In addition, the adoption of the MPC strategy produces better performance results compared to the optimization method without the MPC strategy.