  Hybrid-electric propulsion systems powered by clean energy derived from
renewable sources offer a promising approach to decarbonise the world's
transportation systems. Effective energy management systems are critical for
such systems to achieve optimised operational performance. However, developing
an intelligent energy management system for applications such as ships
operating in a highly stochastic environment and requiring concurrent control
over multiple power sources presents challenges. This article proposes an
intelligent energy management framework for hybrid-electric propulsion systems
using deep reinforcement learning. In the proposed framework, a Twin-Delayed
Deep Deterministic Policy Gradient agent is trained using an extensive volume
of historical load profiles to generate a generic energy management strategy.
The strategy, i.e. the core of the energy management system, can concurrently
control multiple power sources in continuous state and action spaces. The
proposed framework is applied to a coastal ferry model with multiple fuel cell
clusters and a battery, achieving near-optimal cost performance when applied to
novel future voyages.

Systems and Control;Systems and Control