Murong Shan , Shanke Liu , Yibo Wang , Xue'e Wang , Xiantai Zeng , Yinzi Liu , Hao Chen , Chengwei Huang , Lijun Yu
{"title":"Intelligent energy management strategy for fuel cell hybrid vehicles utilizing deep reinforcement learning and driving condition recognition","authors":"Murong Shan , Shanke Liu , Yibo Wang , Xue'e Wang , Xiantai Zeng , Yinzi Liu , Hao Chen , Chengwei Huang , Lijun Yu","doi":"10.1016/j.ijhydene.2025.151769","DOIUrl":null,"url":null,"abstract":"<div><div>Fuel cell hybrid vehicles (FCHVs) require efficient energy management strategies to improve fuel economy and ensure reliable operation under diverse driving conditions. In this study, an adaptive energy management strategy is developed using deep reinforcement learning. A gated recurrent unit model with speed prediction is employed to recognize driving conditions with 97 % accuracy. Based on the identified conditions, a deep deterministic policy gradient framework optimizes continuous power distribution between the fuel cell and battery systems. A tailored reward function is designed to reduce hydrogen consumption and stabilize the battery state of charge. Using real-world driving data for training and validation, the proposed strategy achieves a hydrogen consumption of 1401.38 g, representing a 5–8 % reduction compared with benchmark methods, while maintaining safe state-of-charge operation. These results demonstrate that the proposed method provides improved efficiency, adaptability, and robustness, highlighting its potential as a practical solution for fuel cell hybrid vehicles.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"180 ","pages":"Article 151769"},"PeriodicalIF":8.3000,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Hydrogen Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S036031992504772X","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Fuel cell hybrid vehicles (FCHVs) require efficient energy management strategies to improve fuel economy and ensure reliable operation under diverse driving conditions. In this study, an adaptive energy management strategy is developed using deep reinforcement learning. A gated recurrent unit model with speed prediction is employed to recognize driving conditions with 97 % accuracy. Based on the identified conditions, a deep deterministic policy gradient framework optimizes continuous power distribution between the fuel cell and battery systems. A tailored reward function is designed to reduce hydrogen consumption and stabilize the battery state of charge. Using real-world driving data for training and validation, the proposed strategy achieves a hydrogen consumption of 1401.38 g, representing a 5–8 % reduction compared with benchmark methods, while maintaining safe state-of-charge operation. These results demonstrate that the proposed method provides improved efficiency, adaptability, and robustness, highlighting its potential as a practical solution for fuel cell hybrid vehicles.
期刊介绍:
The objective of the International Journal of Hydrogen Energy is to facilitate the exchange of new ideas, technological advancements, and research findings in the field of Hydrogen Energy among scientists and engineers worldwide. This journal showcases original research, both analytical and experimental, covering various aspects of Hydrogen Energy. These include production, storage, transmission, utilization, enabling technologies, environmental impact, economic considerations, and global perspectives on hydrogen and its carriers such as NH3, CH4, alcohols, etc.
The utilization aspect encompasses various methods such as thermochemical (combustion), photochemical, electrochemical (fuel cells), and nuclear conversion of hydrogen, hydrogen isotopes, and hydrogen carriers into thermal, mechanical, and electrical energies. The applications of these energies can be found in transportation (including aerospace), industrial, commercial, and residential sectors.