IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v209y2020ics0360544220316042.html
   My bibliography  Save this article

Aerodynamic-aware coordinated control of following speed and power distribution for hybrid electric trucks

Author

Listed:
  • Xie, Shaobo
  • Lang, Kun
  • Qi, Shanwei

Abstract

In a truck platoon, a smaller inter-vehicle gap reduces aerodynamic drag, which is beneficial for energy savings, but can also elevate the risk of crashing against the lead vehicle. Therefore, a reasonable speed for the following truck should be planned to balance safety and energy consumption. Moreover, for hybrid electric trucks, optimizing the power split among different energy sources also contributes to energy savings. Following distance and power distribution are therefore coupled. To achieve cost savings in a hybrid truck following situation, a co-optimization should be performed on speed planning and power split to achieve an optimal trade-off between safety, drag reduction, and energy consumption. In this paper, such an approach is developed and analyzed. Model predictive control is adopted to implement the optimization strategy. Real-world speed profiles are used to assess the proposed method, and the results demonstrate that the coordinated control method outperforms a manual following strategy by flexibly tuning the following distance, resulting in cost savings of about 5.2%.

Suggested Citation

  • Xie, Shaobo & Lang, Kun & Qi, Shanwei, 2020. "Aerodynamic-aware coordinated control of following speed and power distribution for hybrid electric trucks," Energy, Elsevier, vol. 209(C).
  • Handle: RePEc:eee:energy:v:209:y:2020:i:c:s0360544220316042
    DOI: 10.1016/j.energy.2020.118496
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220316042
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.118496?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. 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.
    2. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Lang, Kun, 2018. "An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 163(C), pages 837-848.
    3. Barkenbus, Jack N., 2010. "Eco-driving: An overlooked climate change initiative," Energy Policy, Elsevier, vol. 38(2), pages 762-769, February.
    4. Offer, G.J. & Howey, D. & Contestabile, M. & Clague, R. & Brandon, N.P., 2010. "Comparative analysis of battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system," Energy Policy, Elsevier, vol. 38(1), pages 24-29, January.
    5. Xie, Shaobo & Hu, Xiaosong & Liu, Teng & Qi, Shanwei & Lang, Kun & Li, Huiling, 2019. "Predictive vehicle-following power management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 166(C), pages 701-714.
    6. Wang, Hong & Huang, Yanjun & Khajepour, Amir & Song, Qiang, 2016. "Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle," Applied Energy, Elsevier, vol. 182(C), pages 105-114.
    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. Pan, Chaofeng & Huang, Aibao & Wang, Jian & Chen, Liao & Liang, Jun & Zhou, Weiqi & Wang, Limei & Yang, Jufeng, 2022. "Energy-optimal adaptive cruise control strategy for electric vehicles based on model predictive control," Energy, Elsevier, vol. 241(C).
    2. 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).

    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. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Tang, Xiaolin & Lang, Kun & Xin, Zongke & Brighton, James, 2019. "Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge," Energy, Elsevier, vol. 173(C), pages 667-678.
    2. Shaobo Xie & Xiaosong Hu & Kun Lang & Shanwei Qi & Tong Liu, 2018. "Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    3. Xie, Shaobo & Qi, Shanwei & Lang, Kun & Tang, Xiaolin & Lin, Xianke, 2020. "Coordinated management of connected plug-in hybrid electric buses for energy saving, inter-vehicle safety, and battery health," Applied Energy, Elsevier, vol. 268(C).
    4. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    5. 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.
    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. 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.
    8. Li, Yapeng & Wang, Feng & Tang, Xiaolin & Hu, Xiaosong & Lin, Xianke, 2022. "Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 257(C).
    9. Yaqian Wang & Xiaohong Jiao, 2022. "Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-19, April.
    10. Chen, Zhihang & Liu, Yonggang & Zhang, Yuanjian & Lei, Zhenzhen & Chen, Zheng & Li, Guang, 2022. "A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles," Energy, Elsevier, vol. 243(C).
    11. Xiaobo Sun & Weirong Liu & Mengfei Wen & Yue Wu & Heng Li & Jiahao Huang & Chao Hu & Zhiwu Huang, 2021. "A Real-Time Optimal Car-Following Power Management Strategy for Hybrid Electric Vehicles with ACC Systems," Energies, MDPI, vol. 14(12), pages 1-17, June.
    12. Yuan, Jingni & Yang, Lin, 2019. "Predictive energy management strategy for connected 48V hybrid electric vehicles," Energy, Elsevier, vol. 187(C).
    13. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    14. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    15. Ahmed, Sumayyah & Sanguinetti, Angela, 2015. "OBDEnergy: Making Metrics Meaningful in Eco-driving Feedback," Institute of Transportation Studies, Working Paper Series qt0x73t2jw, Institute of Transportation Studies, UC Davis.
    16. Pietro Stabile & Federico Ballo & Giorgio Previati & Giampiero Mastinu & Massimiliano Gobbi, 2023. "Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios," Energies, MDPI, vol. 16(3), pages 1-19, January.
    17. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    18. Behiri, Walid & Belmokhtar-Berraf, Sana & Chu, Chengbin, 2018. "Urban freight transport using passenger rail network: Scientific issues and quantitative analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 227-245.
    19. Ruffini, Eleonora & Wei, Max, 2018. "Future costs of fuel cell electric vehicles in California using a learning rate approach," Energy, Elsevier, vol. 150(C), pages 329-341.
    20. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.

    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:energy:v:209:y:2020:i:c:s0360544220316042. 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.journals.elsevier.com/energy .

    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.