Deep Reinforcement Learning-Based Energy and Power Management for Ships: A Perspective Review of Methods and Applications
TL;DR
It is argued that future shipboard EMS/PMS adopt integrated hierarchical DRL frameworks across all three decision layers, leveraging DRL’s strengths in sequential policy learning in dynamic environments and supporting multi-time-scale decision-making.
Abstract
Energy and power management systems (EMS/PMS) are essential for electric-propulsion ships, affecting propulsion performance, fuel consumption, emissions, and component lifetime. As shipboard power systems integrate heterogeneous energy resources and face nonlinearity, uncertain load demand, and multi-source interactions, deep reinforcement learning (DRL) has emerged as a promising adaptive, sequential decision-making tool in shipboard EMS/PMS. This perspective reviews DRL studies through a hierarchical decision-making framework comprising power dispatch, energy coordination, and operational strategy. Most research focuses on real-time power dispatch, while emerging research addresses energy coordination via multi-source cooperation, multi-objective operation, degradation awareness, and uncertainty handling. However, operational strategy remains underexplored, despite its role in speed control, route-aware planning, predictive operation, and voyage scheduling. This paper argues that future shipboard EMS/PMS adopt integrated hierarchical DRL frameworks across all three decision layers, leveraging DRL’s strengths in sequential policy learning in dynamic environments and supporting multi-time-scale decision-making. This paper clarifies current research trends, identifies gaps, and outlines future directions toward adaptive, reliable, and autonomous shipboard EMS/PMS in next-generation electric-propulsion ships.