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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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).
    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. Rezaei, A. & Burl, J.B. & Solouk, A. & Zhou, B. & Rezaei, M. & Shahbakhti, M., 2017. "Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles," Applied Energy, Elsevier, vol. 208(C), pages 655-665.
    7. 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.
    8. 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.

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