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Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle

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Cited by:

  1. Li, Jianwei & Liu, Jie & Yang, Qingqing & Wang, Tianci & He, Hongwen & Wang, Hanxiao & Sun, Fengchun, 2025. "Reinforcement learning based energy management for fuel cell hybrid electric vehicles: A comprehensive review on decision process reformulation and strategy implementation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 213(C).
  2. Yao He & Changchang Miao & Ji Wu & Xinxin Zheng & Xintian Liu & Xingtao Liu & Feng Han, 2021. "Research on the Power Distribution Method for Hybrid Power System in the Fuel Cell Vehicle," Energies, MDPI, vol. 14(3), pages 1-15, January.
  3. Wu, Yue & Huang, Zhiwu & Liao, Hongtao & Chen, Bin & Zhang, Xiaoyong & Zhou, Yanhui & Liu, Yongjie & Li, Heng & Peng, Jun, 2020. "Adaptive power allocation using artificial potential field with compensator for hybrid energy storage systems in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
  4. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
  5. Imen Jarraya & Fatma Abdelhedi & Nassim Rizoug, 2023. "An Innovative Power Management Strategy for Hybrid Battery–Supercapacitor Systems in Electric Vehicle," Mathematics, MDPI, vol. 12(1), pages 1-23, December.
  6. 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.
  7. Dimitrios Rimpas & Stavrοs D. Kaminaris & Dimitrios D. Piromalis & George Vokas, 2023. "Real-Time Management for an EV Hybrid Storage System Based on Fuzzy Control," Mathematics, MDPI, vol. 11(21), pages 1-18, October.
  8. Xu, Bin & Shi, Junzhe & Li, Sixu & Li, Huayi & Wang, Zhe, 2021. "Energy consumption and battery aging minimization using a Q-learning strategy for a battery/ultracapacitor electric vehicle," Energy, Elsevier, vol. 229(C).
  9. Tengda Hu & Yunwu Li & Zhi Zhang & Ying Zhao & Dexiong Liu, 2021. "Energy Management Strategy of Hybrid Energy Storage System Based on Road Slope Information," Energies, MDPI, vol. 14(9), pages 1-18, April.
  10. Wegmann, Raphael & Döge, Volker & Sauer, Dirk Uwe, 2018. "Assessing the potential of a hybrid battery system to reduce battery aging in an electric vehicle by studying the cycle life of a graphite∣NCA high energy and a LTO∣metal oxide high power battery cell," Applied Energy, Elsevier, vol. 226(C), pages 197-212.
  11. 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.
  12. Lv, Jie & Lin, Shili & Song, Wenji & Chen, Mingbiao & Feng, Ziping & Li, Yongliang & Ding, Yulong, 2019. "Performance of LiFePO4 batteries in parallel based on connection topology," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
  13. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
  14. Xiaogang Wu & Zhihao Cui & Xuefeng Li & Jiuyu Du & Ye Liu, 2019. "Control Strategy for Active Hierarchical Equalization Circuits of Series Battery Packs," Energies, MDPI, vol. 12(11), pages 1-18, May.
  15. 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.
  16. Liu, Xinhua & Ai, Weilong & Naylor Marlow, Max & Patel, Yatish & Wu, Billy, 2019. "The effect of cell-to-cell variations and thermal gradients on the performance and degradation of lithium-ion battery packs," Applied Energy, Elsevier, vol. 248(C), pages 489-499.
  17. Wang, Yujie & Sun, Zhendong & Chen, Zonghai, 2019. "Development of energy management system based on a rule-based power distribution strategy for hybrid power sources," Energy, Elsevier, vol. 175(C), pages 1055-1066.
  18. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.
  19. Ma, Fangwu & Yang, Yu & Wang, Jiawei & Liu, Zhenze & Li, Jinhang & Nie, Jiahong & Shen, Yucheng & Wu, Liang, 2019. "Predictive energy-saving optimization based on nonlinear model predictive control for cooperative connected vehicles platoon with V2V communication," Energy, Elsevier, vol. 189(C).
  20. Berrueta, Alberto & Heck, Michael & Jantsch, Martin & Ursúa, Alfredo & Sanchis, Pablo, 2018. "Combined dynamic programming and region-elimination technique algorithm for optimal sizing and management of lithium-ion batteries for photovoltaic plants," Applied Energy, Elsevier, vol. 228(C), pages 1-11.
  21. Sun, Qixing & Xing, Dong & Alafnan, Hamoud & Pei, Xiaoze & Zhang, Min & Yuan, Weijia, 2019. "Design and test of a new two-stage control scheme for SMES-battery hybrid energy storage systems for microgrid applications," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  22. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
  23. Bo, Lin & Han, Lijin & Xiang, Changle & Liu, Hui & Ma, Tian, 2022. "A Q-learning fuzzy inference system based online energy management strategy for off-road hybrid electric vehicles," Energy, Elsevier, vol. 252(C).
  24. Tian, Yu & Lin, Cheng & Li, Hailong & Du, Jiuyu & Xiong, Rui, 2021. "Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods," Applied Energy, Elsevier, vol. 300(C).
  25. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
  26. Zhang, Wei & Wang, Jixin & Liu, Yong & Gao, Guangzong & Liang, Siwen & Ma, Hongfeng, 2020. "Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery," Applied Energy, Elsevier, vol. 275(C).
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