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Energy management for plug-in hybrid electric vehicles considering optimal engine ON/OFF control and fast state-of-charge trajectory planning

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  • Guo, Ningyuan
  • Shen, Jiangwei
  • Xiao, Renxin
  • Yan, Wensheng
  • Chen, Zheng

Abstract

In this paper, a novel energy management strategy for plug-in hybrid electric vehicles (PHEVs) is proposed, considering engine on/off control and state-of-charge (SOC) trajectory planning. First, the powertrain structure is analyzed in detail, and then the fuel rate is fitted versus the battery power by a series of quadratic equations. Pontryagin's minimum principle is employed to determine the optimal engine on/off switching and battery power commands, and the estimation distribution algorithm is applied to search for the optimal equivalent factor. Numerical simulations and hardware-in-the-loop experiments validate that near-optimal fuel performance can be achieved based on the proposed algorithm with reasonable computational cost. In addition, the SOC reference trajectory optimized by the proposed algorithm is validated to be effective for application with online energy management strategies.

Suggested Citation

  • Guo, Ningyuan & Shen, Jiangwei & Xiao, Renxin & Yan, Wensheng & Chen, Zheng, 2018. "Energy management for plug-in hybrid electric vehicles considering optimal engine ON/OFF control and fast state-of-charge trajectory planning," Energy, Elsevier, vol. 163(C), pages 457-474.
    • Handle: RePEc:eee:energy:v:163:y:2018:i:c:p:457-474
    DOI: 10.1016/j.energy.2018.08.116
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    References listed on IDEAS

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    1. Chen, Zheng & Xia, Bing & You, Chenwen & Mi, Chunting Chris, 2015. "A novel energy management method for series plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 145(C), pages 172-179.
    2. Tribioli, Laura & Cozzolino, Raffaello & Chiappini, Daniele & Iora, Paolo, 2016. "Energy management of a plug-in fuel cell/battery hybrid vehicle with on-board fuel processing," Applied Energy, Elsevier, vol. 184(C), pages 140-154.
    3. Onori, Simona & Tribioli, Laura, 2015. "Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt," Applied Energy, Elsevier, vol. 147(C), pages 224-234.
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    Cited by:

    1. Md. Sazal Miah & Molla Shahadat Hossain Lipu & Sheikh Tanzim Meraj & Kamrul Hasan & Shaheer Ansari & Taskin Jamal & Hasan Masrur & Rajvikram Madurai Elavarasan & Aini Hussain, 2021. "Optimized Energy Management Schemes for Electric Vehicle Applications: A Bibliometric Analysis towards Future Trends," Sustainability, MDPI, vol. 13(22), pages 1-38, November.
    2. Tao Zhang & Ningyuan Guo & Xiaoxia Sun & Jie Fan & Naifeng Yang & Junjie Song & Yuan Zou, 2021. "A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles," Sustainability, MDPI, vol. 13(9), pages 1-19, May.
    3. Guo, Lingxiong & Zhang, Xudong & Zou, Yuan & Guo, Ningyuan & Li, Jianwei & Du, Guodong, 2021. "Cost-optimal energy management strategy for plug-in hybrid electric vehicles with variable horizon speed prediction and adaptive state-of-charge reference," Energy, Elsevier, vol. 232(C).
    4. Guo, Ningyuan & Zhang, Xudong & Zou, Yuan & Guo, Lingxiong & Du, Guodong, 2021. "Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation," Energy, Elsevier, vol. 214(C).
    5. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization," Energy, Elsevier, vol. 204(C).
    6. Wu, Yitao & Zhang, Yuanjian & Li, Guang & Shen, Jiangwei & Chen, Zheng & Liu, Yonggang, 2020. "A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks," Energy, Elsevier, vol. 208(C).

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