IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v281y2021ics0306261920313994.html
   My bibliography  Save this article

Intelligent SOC-consumption allocation of commercial plug-in hybrid electric vehicles in variable scenario

Author

Listed:
  • Cao, Jianfei
  • He, Hongwen
  • Wei, Dong

Abstract

At present, plug-in hybrid electric vehicle has got widely concerned and studied for its special advantages of fuel-saving and emission-reduction potential. In order to improve the electric energy efficiency in power battery and meet the practical requirements of energy management strategies, an intelligent SOC-consumption allocation of commercial plug-in hybrid electric vehicles in variable scenario is proposed in this paper. Generated by random combination of the selected driving cycle units, the variable scene could reflect the driving situation of commercial vehicles performing transportation tasks at some extent. The strategy is trained and generated by a reinforcement learning framework. Firstly, a power-flow analysis model is constructed. Then, a dynamic programming description that could solve the optimal power-flow is defined. Furthermore, the optimizations for different initial conditions were performed repeatedly. With the solution results, an energy-conversion model is generated to describes the conversion of fuel and electricity, and reward in reinforcement learning was defined and used to guide the agent to improve and enhance the required SOC-allocation strategy. The trained strategy performed a reasonable SOC-allocation according to the cycle unit and residual SOC; and fuel consumption under average condition is significantly lower than that of two normally used SOC-allocation strategies.

Suggested Citation

  • Cao, Jianfei & He, Hongwen & Wei, Dong, 2021. "Intelligent SOC-consumption allocation of commercial plug-in hybrid electric vehicles in variable scenario," Applied Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920313994
    DOI: 10.1016/j.apenergy.2020.115942
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920313994
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115942?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Gaopeng & Zhang, Jieli & He, Hongwen, 2017. "Battery SOC constraint comparison for predictive energy management of plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 194(C), pages 578-587.
    2. Lewis, Anne Marie & Kelly, Jarod C. & Keoleian, Gregory A., 2014. "Vehicle lightweighting vs. electrification: Life cycle energy and GHG emissions results for diverse powertrain vehicles," Applied Energy, Elsevier, vol. 126(C), pages 13-20.
    3. Hajipour, Ehsan & Mohiti, Maryam & Farzin, Nima & Vakilian, Mehdi, 2017. "Optimal distribution transformer sizing in a harmonic involved load environment via dynamic programming technique," Energy, Elsevier, vol. 120(C), pages 92-105.
    4. Hongwen, He & Jinquan, Guo & Jiankun, Peng & Huachun, Tan & Chao, Sun, 2018. "Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 95-107.
    5. Hutchinson, Tim & Burgess, Stuart & Herrmann, Guido, 2014. "Current hybrid-electric powertrain architectures: Applying empirical design data to life cycle assessment and whole-life cost analysis," Applied Energy, Elsevier, vol. 119(C), pages 314-329.
    6. Xie, Shanshan & He, Hongwen & Peng, Jiankun, 2017. "An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 196(C), pages 279-288.
    7. Du, Yongchang & Zhao, Yue & Wang, Qinpu & Zhang, Yuanbo & Xia, Huaicheng, 2016. "Trip-oriented stochastic optimal energy management strategy for plug-in hybrid electric bus," Energy, Elsevier, vol. 115(P1), pages 1259-1271.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    2. Li, Jie & Wu, Xiaodong & Xu, Min & Liu, Yonggang, 2022. "Deep reinforcement learning and reward shaping based eco-driving control for automated HEVs among signalized intersections," Energy, Elsevier, vol. 251(C).
    3. Choi, Mingi & Cha, Junepyo & Song, Jingeun, 2024. "Analysis of fuel economy reduction factors of hybrid electric vehicles in winter using on-road driving data," Energy, Elsevier, vol. 289(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Zheng & Hu, Hengjie & Wu, Yitao & Zhang, Yuanjian & Li, Guang & Liu, Yonggang, 2020. "Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 211(C).
    2. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    3. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    4. Björnsson, Lars-Henrik & Karlsson, Sten, 2016. "The potential for brake energy regeneration under Swedish conditions," Applied Energy, Elsevier, vol. 168(C), pages 75-84.
    5. Ye Yang & Youtong Zhang & Jingyi Tian & Si Zhang, 2018. "Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability," Energies, MDPI, vol. 11(8), pages 1-22, August.
    6. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei & Guo, Qiuyi, 2018. "Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction," Energy, Elsevier, vol. 155(C), pages 838-852.
    7. 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).
    8. Shaobo Xie & Huiling Li & Zongke Xin & Tong Liu & Lang Wei, 2017. "A Pontryagin Minimum Principle-Based Adaptive Equivalent Consumption Minimum Strategy for a Plug-in Hybrid Electric Bus on a Fixed Route," Energies, MDPI, vol. 10(9), pages 1-22, September.
    9. Guo, Hongqiang & Lu, Silong & Hui, Hongzhong & Bao, Chunjiang & Shangguan, Jinyong, 2019. "Receding horizon control-based energy management for plug-in hybrid electric buses using a predictive model of terminal SOC constraint in consideration of stochastic vehicle mass," Energy, Elsevier, vol. 176(C), pages 292-308.
    10. Babu, Ajay & Ashok, S., 2015. "Improved parallel mild hybrids for urban roads," Applied Energy, Elsevier, vol. 144(C), pages 276-283.
    11. Lin, Xinyou & Wu, Jiayun & Wei, Yimin, 2021. "An ensemble learning velocity prediction-based energy management strategy for a plug-in hybrid electric vehicle considering driving pattern adaptive reference SOC," Energy, Elsevier, vol. 234(C).
    12. Zhou, Xingyu & Qin, Datong & Hu, Jianjun, 2017. "Multi-objective optimization design and performance evaluation for plug-in hybrid electric vehicle powertrains," Applied Energy, Elsevier, vol. 208(C), pages 1608-1625.
    13. Zhao, Mingjie & Shi, Junhui & Lin, Cheng, 2019. "Optimization of integrated energy management for a dual-motor coaxial coupling propulsion electric city bus," Applied Energy, Elsevier, vol. 243(C), pages 21-34.
    14. 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.
    15. Kong, Yan & Xu, Nan & Liu, Qiao & Sui, Yan & Jia, Yifan, 2024. "Variable horizon-based predictive energy management strategy for plug-in hybrid electric vehicles and determination of a suitable predictive horizon," Energy, Elsevier, vol. 294(C).
    16. Li, Shuangqi & He, Hongwen & Zhao, Pengfei, 2021. "Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective," Energy, Elsevier, vol. 230(C).
    17. 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.
    18. Zhang, Hailong & Peng, Jiankun & Tan, Huachun & Dong, Hanxuan & Ding, Fan & Ran, Bin, 2020. "Tackling SOC long-term dynamic for energy management of hybrid electric buses via adaptive policy optimization," Applied Energy, Elsevier, vol. 269(C).
    19. Zhang, Zhendong & He, Hongwen & Guo, Jinquan & Han, Ruoyan, 2020. "Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 280(C).
    20. Yan, Qing-dong & Chen, Xiu-qi & Jian, Hong-chao & Wei, Wei & Wang, Wei-da & Wang, Heng, 2022. "Design of a deep inference framework for required power forecasting and predictive control on a hybrid electric mining truck," Energy, Elsevier, vol. 238(PC).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920313994. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.