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Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming

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  • Hu, Jiayi
  • Li, Jianqiu
  • Hu, Zunyan
  • Xu, Liangfei
  • Ouyang, Minggao

Abstract

In order to enhance the power and bring additional energy-saving possibilities of heavy-duty vehicles, a novel dual-engine system is utilized in a series hybrid electric vehicle. However, the minimum fuel consumption of this configuration is still unclear, and the effective online energy management strategy for this system is absent. The studied vehicle adopts the dual-engine configuration, which is powered by Li-ion batteries and two auxiliary power units. This paper presents a two-step framework to address the energy management problem. A map-based strategy is adopted to distribute power between engines. The dynamic programming (DP) algorithm is incorporated to distribute power between engines and batteries and find the minimum fuel consumption. Simulation results show that the DP algorithm can save 7.3% fuel compared to the charge depleting and charge sustaining strategy. The dual-engine system achieves a 1.9% lower fuel consumption compared with the conventional hybrid system with one engine. The operation points of engines and the power profiles of batteries derived from the DP are analyzed, and a rule-based strategy is designed correspondingly. The proposed rule-based algorithm can reduce 2.2%–6.0% fuel consumption and is not sensitive to the variations of battery size, parameters of the rule-based strategy, and driving cycles.

Suggested Citation

  • Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
  • Handle: RePEc:eee:energy:v:215:y:2021:i:pa:s0360544220319587
    DOI: 10.1016/j.energy.2020.118851
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    8. Huang, Ying & Wang, Shilong & Li, Ke & Fan, Zhuwei & Xie, Haiming & Jiang, Fachao, 2023. "Multi-parameter adaptive online energy management strategy for concrete truck mixers with a novel hybrid powertrain considering vehicle mass," Energy, Elsevier, vol. 277(C).
    9. Zhou, Jianhao & Xue, Yuan & Xu, Da & Li, Chaoxiong & Zhao, Wanzhong, 2022. "Self-learning energy management strategy for hybrid electric vehicle via curiosity-inspired asynchronous deep reinforcement learning," Energy, Elsevier, vol. 242(C).
    10. Yuguo Xu & Enyong Xu & Weiguang Zheng & Qibai Huang, 2023. "Hierarchical Model-Predictive-Control-Based Energy Management Strategy for Fuel Cell Hybrid Commercial Vehicles Incorporating Traffic Information," Sustainability, MDPI, vol. 15(17), pages 1-21, August.
    11. Wilberforce, Tabbi & Anser, Afaaq & Swamy, Jangam Aishwarya & Opoku, Richard, 2023. "An investigation into hybrid energy storage system control and power distribution for hybrid electric vehicles," Energy, Elsevier, vol. 279(C).
    12. Alcázar-García, Désirée & Romeral Martínez, José Luis, 2022. "Model-based design validation and optimization of drive systems in electric, hybrid, plug-in hybrid and fuel cell vehicles," Energy, Elsevier, vol. 254(PA).
    13. Tiande Mo & Yu Li & Kin-tak Lau & Chi Kin Poon & Yinghong Wu & Yang Luo, 2022. "Trends and Emerging Technologies for the Development of Electric Vehicles," Energies, MDPI, vol. 15(17), pages 1-34, August.
    14. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
    15. Bao, Shuyue & Sun, Ping & Zhu, Jianxin & Ji, Qian & Liu, Junheng, 2022. "Improved multi-dimensional dynamic programming energy management strategy for a vehicle power-split hybrid powertrain," Energy, Elsevier, vol. 256(C).
    16. Yongjian Zhou & Rong Yang & Song Zhang & Kejun Lan & Wei Huang, 2023. "Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks," Energies, MDPI, vol. 16(17), pages 1-21, August.
    17. Zhang, Zhen & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Yang, Jian & Jia, Qingxiao, 2023. "Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle," Energy, Elsevier, vol. 269(C).
    18. Wang, Zhuang & Chen, Li & Wang, Bin & Huang, Lianzhong & Wang, Kai & Ma, Ranqi, 2023. "Integrated optimization of speed schedule and energy management for a hybrid electric cruise ship considering environmental factors," Energy, Elsevier, vol. 282(C).
    19. Changqing Du & Shiyang Huang & Yuyao Jiang & Dongmei Wu & Yang Li, 2022. "Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming," Energies, MDPI, vol. 15(12), pages 1-25, June.
    20. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    21. Du, Yi & Cui, Naxin & Cui, Wei & Li, Tao & Ren, Fei & Zhang, Chenghui, 2023. "AGRU and convex optimization based energy management for plug-in hybrid electric bus considering battery aging," Energy, Elsevier, vol. 277(C).

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