| Abstract:The energy management strategy, as the top-level control of a hybrid aircraft propulsion system, is utilized to distribute
energy among different power sources. It is considered the foundation for ensuring the efficient operation of the system. The energy manage?
ment strategies of various types of hybrid aircraft propulsion systems were elaborately discussed, and the characteristics and research status
of three types of energy management strategies based on rules, optimization, and learning were systematically summarized. By describing
the principle of reinforcement learning, the reward performance, neural network updating principle, respective advantages and
disadvantages, and applicable scenarios of the deep Q-network algorithm and deep deterministic policy gradient algorithm were analyzed.
It is pointed out that the deficiency of rule-based energy management strategies, which relied heavily on expert experiences, can be
mitigated by integrating with learning-based approaches. On this basis, the future development trends of energy management strategies are
envisioned to focus on internal innovation of learning-based algorithms and integration innovation with different types of algorithms. This
can provide references for subsequent research on energy management strategies in hybrid aircraft propulsion systems. |
