Deep reinforcement learning-based energy management strategy for fuel cell buses integrating future road information and cabin comfort control

被引：0
|
作者：
Jia, Chunchun [1 ,2 ]
Liu, Wei [1 ,2 ]
He, Hongwen [3 ]
Chau, K. T. [1 ,2 ]
机构：
[1] Hong Kong Polytech Univ, Res Ctr Elect Vehicles, Hong Kong 999077, Peoples R China
[2] Hong Kong Polytech Univ, Dept Elect & Elect Engn, Hong Kong 999077, Peoples R China
[3] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China
来源：
ENERGY CONVERSION AND MANAGEMENT | 2024年 / 321卷
关键词：
Fuel cell bus; Energy management strategy; Multi-source information fusion; Cabin comfort control; Deep reinforcement learning; ELECTRIC VEHICLES; POWER MANAGEMENT; SYSTEM;
D O I：
10.1016/j.enconman.2024.119032
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
Conventional energy management strategy (EMS) for fuel cell vehicles (FCVs) aims to optimize powertrain energy consumption while ignoring the air conditioning regulation, whereby the overall energy efficiency cannot be optimal. To enhance the cabin-powertrain holistic energy utilization without compromising energy storage system degradation and passenger temperature comfort, this paper proposes a novel energy management paradigm. The comprehensive control of cabin comfort and fuel cell/battery durability is achieved by comprehensively utilizing onboard sensors and vehicle-cloud infrastructure. Specifically, the vehicle energy- and thermal-coupled control problem is formulated by considering energy consumption, component ageing, and cabin's dynamic thermal model. In addition to regular state space in energy management problems, future road information and environmental temperature are innovatively integrated into the energy management framework. A twin delayed deep deterministic policy gradient algorithm is used to solve the problem to enhance the overall energy efficiency. Simulation results indicate that, compared with rule-based EMSs, the proposed strategy achieves cabin comfort while extending the battery life by at least 3.79 % and reducing the overall vehicle operating cost by at least 2.71 %.
引用
收藏
页数：12
相关论文
共 50 条
  • [41] Research on Deep Reinforcement Learning-based Intelligent Car-following Control and Energy Management Strategy for Hybrid Electric Vehicles
    Tang X.
    Chen J.
    Liu T.
    Li J.
    Hu X.
    Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2021, 57 (22): : 237 - 246
  • [42] A Novel Minimal-Cost Power Allocation Strategy for Fuel Cell Hybrid Buses Based on Deep Reinforcement Learning Algorithms
    Li, Kunang
    Jia, Chunchun
    Han, Xuefeng
    He, Hongwen
    SUSTAINABILITY, 2023, 15 (10)
  • [43] Large-scale multi-agent deep reinforcement learning-based coordination strategy for energy optimization and control of proton exchange membrane fuel cell
    Li, Jiawen
    Yu, Tao
    SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2021, 48
  • [44] Energy Management Strategy Based on Reinforcement Learning and Frequency Decoupling for Fuel Cell Hybrid Powertrain
    Li, Hongzhe
    Kang, Jinsong
    Li, Cheng
    ENERGIES, 2024, 17 (08)
  • [45] Energy management optimization of fuel cell hybrid electric vehicle based on deep reinforcement learning
    Wang, Hao-Cong
    Wang, Yue-Yang
    Fu, Zhu-Mu
    Chen, Qi-Hong
    Tao, Fa-Zhan
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2024, 41 (10): : 1831 - 1841
  • [46] Deep Reinforcement Learning-based Strategy for Active Flow Control of Bridge Deck
    Deng X.-L.
    Hu G.
    Chen W.-L.
    Ou J.-P.
    Zhongguo Gonglu Xuebao/China Journal of Highway and Transport, 2023, 36 (08): : 66 - 75
  • [47] Collaborative Optimization of Energy Management Strategy and Adaptive Cruise Control Based on Deep Reinforcement Learning
    Peng, Jiankun
    Fan, Yi
    Yin, Guodong
    Jiang, Ruhai
    IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 2023, 9 (01) : 34 - 44
  • [48] Optimization Strategy Based on Deep Reinforcement Learning for Home Energy Management
    Liu, Yuankun
    Zhang, Dongxia
    Gooi, Hoay Beng
    CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, 2020, 6 (03): : 572 - 582
  • [49] Optimal control strategy for solid oxide fuel cell-based hybrid energy system using deep reinforcement learning
    Chen, Tao
    Gao, Ciwei
    Song, Yutong
    IET RENEWABLE POWER GENERATION, 2022, 16 (05) : 912 - 921
  • [50] Energy management strategy of fuel cell vehicles with hybrid energy sources: A novel framework via deep reinforcement learning and transfer learning
    Zhou, Jianhao
    Guo, Aijun
    Wang, Jie
    Wang, Chunyan
    Zhao, Wanzhong
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2023, 238 (14) : 4659 - 4675
12345