Fan, Ailong;
Liu, Hanyou;
Wu, Peng;
Yang, Liu;
Guan, Cong;
Li, Taotao;
Bucknall, Richard;
(2025)
LSTM-Augmented DRL for Generalisable Energy Management of Hydrogen-Hybrid Ship Propulsion Systems.
eTransportation
, Article 100442. 10.1016/j.etran.2025.100442.
(In press).
![[thumbnail of LSTM-Augmented DRL for Generalisable Energy Management of Hydrogen-Hybrid Ship Propulsion Systems.pdf]](https://discovery.ucl.ac.uk/style/images/fileicons/text.png) |
Text
LSTM-Augmented DRL for Generalisable Energy Management of Hydrogen-Hybrid Ship Propulsion Systems.pdf - Accepted Version Access restricted to UCL open access staff until 27 June 2026. Download (5MB) |
Abstract
Enhancing the generalisation of energy management strategies is crucial for hybrid ship power systems to adapt to unknown navigation conditions effectively. A long short-term memory (LSTM)-based data augmentation method is employed to mitigate uncertainty in propulsion power, thereby enhancing the generalisation of energy management strategies based on deep reinforcement learning (DRL). Simulations using a hybrid propulsion model and operational data from “Three Gorges Hydrogen Boat No.1” compared DQN and DDPG algorithms with and without LSTM integration. By evaluating the DRL strategy’s performance in reducing fuel cell operating pressure and energy consumption before and after data augmentation, the quality of generalisation performance is characterised. Results show that optimisation target weights affect training convergence and performance under unknown test conditions. Data enhancement via the LSTM model improves DRL generalisation in unknown navigation conditions. Compared to original DDPG, LSTM-DDPG reduces FC operating pressure by 5.82% and 1.86%, and cuts hydrogen consumption by 0.80% and 2.13% under two days of unknown conditions. This research offers guidance for designing energy management strategies with high generalisation, addressing adaptability issues with real-world data uncertainty.
| Type: | Article |
|---|---|
| Title: | LSTM-Augmented DRL for Generalisable Energy Management of Hydrogen-Hybrid Ship Propulsion Systems |
| DOI: | 10.1016/j.etran.2025.100442 |
| Publisher version: | https://doi.org/10.1016/j.etran.2025.100442 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | hydrogen fuel cell, hybrid power system, ship energy management, DDPG, LSTM, generalisation performance |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10210606 |
Archive Staff Only
 |
View Item |
