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Online Markov Chain-based energy management for a hybrid tracked vehicle with speedy Q-learning

Citations

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Cited by:

  1. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  2. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
  3. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
  4. Matteo Acquarone & Claudio Maino & Daniela Misul & Ezio Spessa & Antonio Mastropietro & Luca Sorrentino & Enrico Busto, 2023. "Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control," Energies, MDPI, vol. 16(6), pages 1-22, March.
  5. Yang, Ningkang & Han, Lijin & Xiang, Changle & Liu, Hui & Li, Xunmin, 2021. "An indirect reinforcement learning based real-time energy management strategy via high-order Markov Chain model for a hybrid electric vehicle," Energy, Elsevier, vol. 236(C).
  6. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  7. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
  8. Lin, Xinyou & Xia, Yutian & Huang, Wei & Li, Hailin, 2021. "Trip distance adaptive power prediction control strategy optimization for a Plug-in Fuel Cell Electric Vehicle," Energy, Elsevier, vol. 224(C).
  9. Loïc Joud & Rui Da Silva & Daniela Chrenko & Alan Kéromnès & Luis Le Moyne, 2020. "Smart Energy Management for Series Hybrid Electric Vehicles Based on Driver Habits Recognition and Prediction," Energies, MDPI, vol. 13(11), pages 1-17, June.
  10. Kakkar, Riya & Agrawal, Smita & Tanwar, Sudeep, 2024. "A systematic survey on demand response management schemes for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
  11. Qin, Haosen & Meng, Tao & Chen, Kan & Li, Zhengwei, 2024. "A comparative study of DQN and D3QN for HVAC system optimization control," Energy, Elsevier, vol. 307(C).
  12. Zhao, Yinghua & Huang, Siqi & Wang, Xiaoyu & Shi, Jingwu & Yao, Shouwen, 2024. "Energy management with adaptive moving average filter and deep deterministic policy gradient reinforcement learning for fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 312(C).
  13. Qin, Haosen & Yu, Zhen & Li, Tailu & Liu, Xueliang & Li, Li, 2023. "Energy-efficient heating control for nearly zero energy residential buildings with deep reinforcement learning," Energy, Elsevier, vol. 264(C).
  14. Lijin Han & Wenhui Shi & Ningkang Yang, 2025. "An Adaptive Energy Management Strategy for Off-Road Hybrid Tracked Vehicles," Energies, MDPI, vol. 18(6), pages 1-15, March.
  15. Li, Wei & Lu, Can, 2019. "The multiple effectiveness of state natural gas consumption constraint policies for achieving sustainable development targets in China," Applied Energy, Elsevier, vol. 235(C), pages 685-698.
  16. Maroto Estrada, Pedro & de Lima, Daniela & Bauer, Peter H. & Mammetti, Marco & Bruno, Joan Carles, 2023. "Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach," Applied Energy, Elsevier, vol. 329(C).
  17. Han, Lijin & Yang, Ke & Ma, Tian & Yang, Ningkang & Liu, Hui & Guo, Lingxiong, 2022. "Battery life constrained real-time energy management strategy for hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 259(C).
  18. Dingyi Guo & Guangyin Lei & Huichao Zhao & Fang Yang & Qiang Zhang, 2024. "Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 17(24), pages 1-21, December.
  19. repec:plo:pone00:0222448 is not listed on IDEAS
  20. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
  21. Piotr Wróblewski & Wojciech Drożdż & Wojciech Lewicki & Paweł Miązek, 2021. "Methodology for Assessing the Impact of Aperiodic Phenomena on the Energy Balance of Propulsion Engines in Vehicle Electromobility Systems for Given Areas," Energies, MDPI, vol. 14(8), pages 1-24, April.
  22. Li, Ji & Zhou, Quan & He, Yinglong & Shuai, Bin & Li, Ziyang & Williams, Huw & Xu, Hongming, 2019. "Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  23. Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.
  24. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
  25. Xiao, Boyi & Yang, Weiwei & Wu, Jiamin & Walker, Paul D. & Zhang, Nong, 2022. "Energy management strategy via maximum entropy reinforcement learning for an extended range logistics vehicle," Energy, Elsevier, vol. 253(C).
  26. Matteo Repetto & Massimiliano Passalacqua & Luis Vaccaro & Mario Marchesoni & Alessandro Pini Prato, 2020. "Turbocompound Power Unit Modelling for a Supercapacitor-Based Series Hybrid Vehicle Application," Energies, MDPI, vol. 13(2), pages 1-20, January.
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