Energy Management Strategy for Diesel–Electric Hybrid Ship Considering Sailing Route Division Based on DDPG

Abstract

The continuous evolution of the shipping industry has resulted in increasingly severe environmental pollution. Hybrid ships can be a promising solution. However, for certain aspects such as the complex power structure of hybrid ships and the different characteristics of energy sources, it is difficult to control the energy conversion and transmission process and to reasonably allocate the power output of each energy unit. In this study, an improved K-means++ algorithm was newly adopted to divide the ship’s route. Subsequently, an energy management strategy (EMS) based on the Deep Deterministic Policy Gradient (DDPG) algorithm for diesel–electric hybrid ships was established. Based on the current navigation environment, the EMS can allocate the output power of each energy unit, and optimize the working space of the diesel engine in real time. The simulation results show that the DDPG-based EMS can reduce fuel consumption by 2.3%, 5.5%, 18.1%, and 20.1% with respect to the results of the Deep Q Network (DQN), Q Learning, HAAR, and rule-based algorithms, respectively.