Nonlinear Model Predictive Control Energy Management Strategy for Hybrid Power Ships Based on Working Condition Identification

Hybrid power technology for ships is an effective way to promote the green and low-carbon development of the maritime industry. The development of pattern recognition technology provides new research ideas for the rational allocation and utilization of energy in hybrid power ships. To reduce fuel consumption, a nonlinear model predictive control energy management strategy based on working condition identification is proposed for optimal energy management to solve the problem of real-time optimal adjustment of generators and batteries. The core of the strategy is to identify the ship's working conditions and the nonlinear model predictive control algorithm. Firstly, to achieve the working condition identification task, a ship working condition dataset based on a hybrid supply power ship data is constructed. The labeled dataset is trained using deep learning techniques. Secondly, based on the identification results, a nonlinear model predictive control algorithm is designed to adjust the generator speed and the battery current to achieve energy optimization control under constraints. Finally, the effectiveness of the proposed strategy in optimizing energy control and reducing fuel consumption is verified through simulation. The proposed strategy can reduce the generator fuel consumption by 5.5% under no noise disturbance when compared with conventional predictive control. Under 10% noise disturbance, it is still able to reduce the fuel consumption by 2.6%.