In this paper, a hybrid ship powered by diesel generator sets and power batteries in series is considered. By analyzing the characteristics of hybrid ship cycle operating conditions, the load power of the hybrid ship under load uncertainty is firstly predicted. Then, considering the economy, emissions and continuous navigation time (endurance) of the hybrid ship, an energy optimization strategy based on the predicted load power is proposed to achieve the goal of minimum fuel consumption, minimum emissions and maximum endurance of ship operation. The experimental results show that, compared with the fuzzy logic rules based strategy, the fuel economy of the ship is increased by 9.6% and the ship’s endurance is increased by 24% for the proposed strategy.

The azimuth thruster is widely used in electric propulsion ships due to its excellent performance. The thrust allocation (TA) method of multi-azimuth thruster is the key technology in ship motion control. The purpose of TA is to accurately distribute the thrust and angle of each thruster to provide the vessel the required force and moment. A TA strategy based on the improved non-dominated sorting genetic algorithm II (INSGA-II) is developed in this study. The algorithm introduces the differential mutation operator in the differential evolution (DE) to replace the polynomial variation in NSGA-II, which improves the local optimization ability of the algorithm. The effectiveness of the TA strategy based on INSGA-II algorithm is illustrated by simulations.