IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v169y2016icp748-756.html
   My bibliography  Save this article

An optimal power management system for a regenerative auxiliary power system for delivery refrigerator trucks

Author

Listed:
  • Mohagheghi Fard, Soheil
  • Khajepour, Amir

Abstract

Engine idling of refrigerator trucks during loading and unloading contributes to greenhouse gas emissions due to their increased fuel consumption. This paper proposes a new anti-idling system that uses two sources of power, battery and engine-driven generator, to run the compressor of the refrigeration system. Therefore, idling can be eliminated because the engine is turned OFF and the battery supplies auxiliary power when the vehicle is stopped for loading or unloading. This system also takes advantage of regenerative braking for increased fuel savings. The power management of this system needs to satisfy two requirements: it must minimize fuel consumption in the whole cycle and must ensure that the battery has enough energy for powering the refrigeration system when the engine is OFF. To meet these objectives, a two-level controller is proposed. In the higher level of this controller, a fast dynamic programming technique that utilizes extracted statistical features of drive and duty cycles of a refrigerator truck is used to find suboptimal values of the initial and final SOC of any two consecutive loading/unloading stops. The lower level of the controller employs an adaptive equivalent fuel consumption minimization (A-ECMS) to determine the split ratio of auxiliary power between the generator and battery for each segment with initial and final SOC obtained by the high-level controller. The simulation results confirm that this new system can eliminate idling of refrigerator trucks and reduce their fuel consumption noticeably such that the cost of replacing components is recouped in a short period of time.

Suggested Citation

  • Mohagheghi Fard, Soheil & Khajepour, Amir, 2016. "An optimal power management system for a regenerative auxiliary power system for delivery refrigerator trucks," Applied Energy, Elsevier, vol. 169(C), pages 748-756.
  • Handle: RePEc:eee:appene:v:169:y:2016:i:c:p:748-756
    DOI: 10.1016/j.apenergy.2016.02.078
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916302161
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2016.02.078?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. Torres, J.L. & Gonzalez, R. & Gimenez, A. & Lopez, J., 2014. "Energy management strategy for plug-in hybrid electric vehicles. A comparative study," Applied Energy, Elsevier, vol. 113(C), pages 816-824.
    2. He, Hongwen & Xiong, Rui & Zhao, Kai & Liu, Zhentong, 2013. "Energy management strategy research on a hybrid power system by hardware-in-loop experiments," Applied Energy, Elsevier, vol. 112(C), pages 1311-1317.
    3. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    4. Wu, Xiaolan & Cao, Binggang & Li, Xueyan & Xu, Jun & Ren, Xiaolong, 2011. "Component sizing optimization of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 88(3), pages 799-804, March.
    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. Antonia Tamborrino & Claudio Perone & Filippo Catalano & Giacomo Squeo & Francesco Caponio & Biagio Bianchi, 2019. "Modelling Energy Consumption and Energy-Saving in High-Quality Olive Oil Decanter Centrifuge: Numerical Study and Experimental Validation," Energies, MDPI, vol. 12(13), pages 1-20, July.
    2. 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).
    3. Li, Junqiu & Wang, Yihe & Chen, Jianwen & Zhang, Xiaopeng, 2017. "Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 363-375.
    4. Fard, Soheil Mohagheghi & Huang, Yanjun & Khazraee, Milad & Khajepour, Amir, 2017. "A novel anti-idling system for service vehicles," Energy, Elsevier, vol. 127(C), pages 650-659.
    5. Wang, Yaxin & Lou, Diming & Xu, Ning & Fang, Liang & Tan, Piqiang, 2021. "Energy management and emission control for range extended electric vehicles," Energy, Elsevier, vol. 236(C).
    6. Huang, Yanjun & Khajepour, Amir & Wang, Hong, 2016. "A predictive power management controller for service vehicle anti-idling systems without a priori information," Applied Energy, Elsevier, vol. 182(C), pages 548-557.
    7. Angelo Maiorino & Fabio Petruzziello & Ciro Aprea, 2021. "Refrigerated Transport: State of the Art, Technical Issues, Innovations and Challenges for Sustainability," Energies, MDPI, vol. 14(21), pages 1-55, November.

    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. Tian, He & Lu, Ziwang & Wang, Xu & Zhang, Xinlong & Huang, Yong & Tian, Guangyu, 2016. "A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus," Applied Energy, Elsevier, vol. 177(C), pages 71-80.
    2. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    3. Cordiner, Stefano & Galeotti, Matteo & Mulone, Vincenzo & Nobile, Matteo & Rocco, Vittorio, 2016. "Trip-based SOC management for a plugin hybrid electric vehicle," Applied Energy, Elsevier, vol. 164(C), pages 891-905.
    4. Du, Jiuyu & Chen, Jingfu & Song, Ziyou & Gao, Mingming & Ouyang, Minggao, 2017. "Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness," Energy, Elsevier, vol. 121(C), pages 32-42.
    5. Hung, Yi-Hsuan & Tung, Yu-Ming & Chang, Chun-Hsin, 2016. "Optimal control of integrated energy management/mode switch timing in a three-power-source hybrid powertrain," Applied Energy, Elsevier, vol. 173(C), pages 184-196.
    6. Zhou, Quan & Zhang, Wei & Cash, Scott & Olatunbosun, Oluremi & Xu, Hongming & Lu, Guoxiang, 2017. "Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization," Applied Energy, Elsevier, vol. 189(C), pages 588-601.
    7. Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    8. Chambon, Paul & Curran, Scott & Huff, Shean & Love, Lonnie & Post, Brian & Wagner, Robert & Jackson, Roderick & Green, Johney, 2017. "Development of a range-extended electric vehicle powertrain for an integrated energy systems research printed utility vehicle," Applied Energy, Elsevier, vol. 191(C), pages 99-110.
    9. Hung, Yi-Hsuan & Wu, Chien-Hsun, 2015. "A combined optimal sizing and energy management approach for hybrid in-wheel motors of EVs," Applied Energy, Elsevier, vol. 139(C), pages 260-271.
    10. Xu, Liangfei & Mueller, Clemens David & Li, Jianqiu & Ouyang, Minggao & Hu, Zunyan, 2015. "Multi-objective component sizing based on optimal energy management strategy of fuel cell electric vehicles," Applied Energy, Elsevier, vol. 157(C), pages 664-674.
    11. Li, Guozhen & Zhang, Zhenyu & Shi, Wankai & Li, Wenyong, 2023. "Energy management strategy and simulation analysis of a hybrid train based on a comprehensive efficiency optimization," Applied Energy, Elsevier, vol. 349(C).
    12. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    13. Xu, Nan & Kong, Yan & Yan, Jinyue & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2022. "Global optimization energy management for multi-energy source vehicles based on “Information layer - Physical layer - Energy layer - Dynamic programming” (IPE-DP)," Applied Energy, Elsevier, vol. 312(C).
    14. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    15. Li, Liang & You, Sixiong & Yang, Chao & Yan, Bingjie & Song, Jian & Chen, Zheng, 2016. "Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 162(C), pages 868-879.
    16. Qin, Zhaobo & Luo, Yugong & Zhuang, Weichao & Pan, Ziheng & Li, Keqiang & Peng, Huei, 2018. "Simultaneous optimization of topology, control and size for multi-mode hybrid tracked vehicles," Applied Energy, Elsevier, vol. 212(C), pages 1627-1641.
    17. Chen, Syuan-Yi & Hung, Yi-Hsuan & Wu, Chien-Hsun & Huang, Siang-Ting, 2015. "Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization," Applied Energy, Elsevier, vol. 160(C), pages 132-145.
    18. Liu, Hui & Li, Xunming & Wang, Weida & Han, Lijin & Xiang, Changle, 2018. "Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 427-444.
    19. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    20. Dimitrova, Zlatina & Maréchal, François, 2015. "Techno-economic design of hybrid electric vehicles using multi objective optimization techniques," Energy, Elsevier, vol. 91(C), pages 630-644.

    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:169:y:2016:i:c:p:748-756. 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.