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Energy Management Strategy for Hybrid Electric Vehicle Based on Driving Condition Identification Using KGA-Means

Author

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  • Shuxian Li

    (State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

  • Minghui Hu

    (State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

  • Changchao Gong

    (State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

  • Sen Zhan

    (Chongqing Changan Automobile Co., Ltd., Chongqing 400023, China)

  • Datong Qin

    (State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

Abstract

In order to solve the problem related to adaptive energy management strategies based on driving condition identification being difficult to be applied to a real hybrid electric vehicle (HEV) controller, this paper proposes an energy management strategy by combining the driving condition identification algorithm based on genetic optimized K-means clustering algorithm (KGA-means), and the equivalent consumption minimization strategy (ECMS). The simulation results show that compared with ECMS, the energy management strategy proposed in this article drives the engine working point closer to the best efficiency curve, and smooths out the state of charge (SOC) change and better maintains the SOC in a highly efficient area. As a result, the vehicle fuel consumption reduces by 6.84%.

Suggested Citation

  • Shuxian Li & Minghui Hu & Changchao Gong & Sen Zhan & Datong Qin, 2018. "Energy Management Strategy for Hybrid Electric Vehicle Based on Driving Condition Identification Using KGA-Means," Energies, MDPI, vol. 11(6), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1531-:d:152133
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    Cited by:

    1. Shi, Wenzhuo & Huangfu, Yigeng & Xu, Liangcai & Pang, Shengzhao, 2022. "Online energy management strategy considering fuel cell fault for multi-stack fuel cell hybrid vehicle based on multi-agent reinforcement learning," Applied Energy, Elsevier, vol. 328(C).
    2. Wang, Siyang & Lin, Xianke, 2020. "Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios," Applied Energy, Elsevier, vol. 271(C).
    3. Andrzej Łebkowski, 2018. "Steam and Oxyhydrogen Addition Influence on Energy Usage by Range Extender—Battery Electric Vehicles," Energies, MDPI, vol. 11(9), pages 1-20, September.
    4. Tran, Dai-Duong & Vafaeipour, Majid & El Baghdadi, Mohamed & Barrero, Ricardo & Van Mierlo, Joeri & Hegazy, Omar, 2020. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    5. 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.
    6. Wang, Yue & Zeng, Xiaohua & Song, Dafeng, 2020. "Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information," Energy, Elsevier, vol. 199(C).
    7. Nie, Zhigen & Jia, Yuan & Wang, Wanqiong & Chen, Zheng & Outbib, Rachid, 2022. "Co-optimization of speed planning and energy management for intelligent fuel cell hybrid vehicle considering complex traffic conditions," Energy, Elsevier, vol. 247(C).
    8. Xin Zhang & Jianhua Yang & Weizhou Wang & Man Zhang & Tianjun Jing, 2018. "Integrated Optimal Dispatch of a Rural Micro-Energy-Grid with Multi-Energy Stream Based on Model Predictive Control," Energies, MDPI, vol. 11(12), pages 1-23, December.
    9. Li, Weihan & Cui, Han & Nemeth, Thomas & Jansen, Jonathan & Ünlübayir, Cem & Wei, Zhongbao & Feng, Xuning & Han, Xuebing & Ouyang, Minggao & Dai, Haifeng & Wei, Xuezhe & Sauer, Dirk Uwe, 2021. "Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning," Applied Energy, Elsevier, vol. 293(C).
    10. Pei Zhang & Xianpan Wu & Changqing Du & Hongming Xu & Huawu Wang, 2020. "Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization," Energies, MDPI, vol. 13(20), pages 1-20, October.
    11. Wu, Peng & Partridge, Julius & Bucknall, Richard, 2020. "Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships," Applied Energy, Elsevier, vol. 275(C).
    12. Rajput, Daizy & Herreros, Jose M. & Innocente, Mauro S. & Bryans, Jeremy & Schaub, Joschka & Dizqah, Arash M., 2022. "Impact of the number of planetary gears on the energy efficiency of electrified powertrains," Applied Energy, Elsevier, vol. 323(C).
    13. López-Ibarra, Jon Ander & Gaztañaga, Haizea & Saez-de-Ibarra, Andoni & Camblong, Haritza, 2020. "Plug-in hybrid electric buses total cost of ownership optimization at fleet level based on battery aging," Applied Energy, Elsevier, vol. 280(C).
    14. Li, Jiawen, 2022. "A multi-objective energy coordinative and management policy for solid oxide fuel cell using triune brain large-scale multi-agent deep deterministic policy gradient," Applied Energy, Elsevier, vol. 324(C).
    15. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
    16. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    17. Zhang, Hanyu & Du, Lili, 2023. "Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part I: Modeling and solution algorithm design," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 174-198.

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