IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v127y2017icp650-659.html
   My bibliography  Save this article

A novel anti-idling system for service vehicles

Author

Listed:
  • Fard, Soheil Mohagheghi
  • Huang, Yanjun
  • Khazraee, Milad
  • Khajepour, Amir

Abstract

This paper presents a novel anti-idling system, namely, a regenerative auxiliary power system (RAPS) for service vehicles. Auxiliary devices, such as a refrigeration system in a food delivery truck, require the engine to idle for providing auxiliary power while the truck stops for loading or unloading. By electrifying auxiliary systems, a battery pack can supply the auxiliary load, thereby reducing engine idling. The main advantages of the proposed anti-idling system over existing technologies are the optimal design and optimal performance (smart charging strategy) which lead to lower overall cost and less fuel consumption. The size of the components in the proposed system is optimized by a multidisciplinary design optimization approach to meet the conditions of compactness, modularity, and ease of installation. By introducing the anti-idling system to a service vehicle, its powertrain becomes hybrid due to the addition of a battery pack. Therefore, to optimize the efficiency, a power management system is developed to decide when and how to charge the battery. This controller operates based on the duty cycle that can be obtained by the proposed prediction method.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:127:y:2017:i:c:p:650-659
    DOI: 10.1016/j.energy.2017.04.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217305832
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.04.018?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. Abdel-Monem, Mohamed & Trad, Khiem & Omar, Noshin & Hegazy, Omar & Van den Bossche, Peter & Van Mierlo, Joeri, 2017. "Influence analysis of static and dynamic fast-charging current profiles on ageing performance of commercial lithium-ion batteries," Energy, Elsevier, vol. 120(C), pages 179-191.
    2. Huang, Yanjun & Fard, Soheil Mohagheghi & Khazraee, Milad & Wang, Hong & Khajepour, Amir, 2017. "An adaptive model predictive controller for a novel battery-powered anti-idling system of service vehicles," Energy, Elsevier, vol. 127(C), pages 318-327.
    3. Khayyam, Hamid & Bab-Hadiashar, Alireza, 2014. "Adaptive intelligent energy management system of plug-in hybrid electric vehicle," Energy, Elsevier, vol. 69(C), pages 319-335.
    4. Liu, Haoye & Wang, Zhi & Wang, Jianxin & He, Xin, 2016. "Improvement of emission characteristics and thermal efficiency in diesel engines by fueling gasoline/diesel/PODEn blends," Energy, Elsevier, vol. 97(C), pages 105-112.
    5. 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.
    6. Battke, Benedikt & Schmidt, Tobias S. & Grosspietsch, David & Hoffmann, Volker H., 2013. "A review and probabilistic model of lifecycle costs of stationary batteries in multiple applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 240-250.
    7. Huang, Yanjun & Khajepour, Amir & Ding, Haitao & Bagheri, Farshid & Bahrami, Majid, 2017. "An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 188(C), pages 576-585.
    8. Clenci, Adrian Constantin & Iorga-Simăn, Victor & Deligant, Michael & Podevin, Pierre & Descombes, Georges & Niculescu, Rodica, 2014. "A CFD (computational fluid dynamics) study on the effects of operating an engine with low intake valve lift at idle corresponding speed," Energy, Elsevier, vol. 71(C), pages 202-217.
    9. Chen, Zeyu & Xiong, Rui & Cao, Jiayi, 2016. "Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions," Energy, Elsevier, vol. 96(C), pages 197-208.
    10. Lutsey, Nicholas & Brodrick, Christie-Joy & Lipman, Timothy, 2007. "Analysis of potential fuel consumption and emissions reductions from fuel cell auxiliary power units (APUs) in long-haul trucks," Energy, Elsevier, vol. 32(12), pages 2428-2438.
    11. 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.
    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. Huang, Yanjun & Wang, Hong & Khajepour, Amir & Li, Bin & Ji, Jie & Zhao, Kegang & Hu, Chuan, 2018. "A review of power management strategies and component sizing methods for hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 132-144.
    2. Nitin S. Solke & Pritesh Shah & Ravi Sekhar & T. P. Singh, 2022. "Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 89-112, March.

    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. Zeyu Chen & Jiahuan Lu & Bo Liu & Nan Zhou & Shijie Li, 2020. "Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries," Energies, MDPI, vol. 13(10), pages 1-15, May.
    2. Wang, Yue & Zeng, Xiaohua & Song, Dafeng & Yang, Nannan, 2019. "Optimal rule design methodology for energy management strategy of a power-split hybrid electric bus," Energy, Elsevier, vol. 185(C), pages 1086-1099.
    3. Li, Ji & Zhou, Quan & He, Yinglong & Shuai, Bin & Li, Ziyang & Williams, Huw & Xu, Hongming, 2019. "Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Li, Maobing & Xu, Hui & Li, Weimin & Liu, Yin & Li, Fade & Hu, Yue & Liu, Li, 2016. "The structure and control method of hybrid power source for electric vehicle," Energy, Elsevier, vol. 112(C), pages 1273-1285.
    5. Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
    6. Chen, Syuan-Yi & Wu, Chien-Hsun & Hung, Yi-Hsuan & Chung, Cheng-Ta, 2018. "Optimal strategies of energy management integrated with transmission control for a hybrid electric vehicle using dynamic particle swarm optimization," Energy, Elsevier, vol. 160(C), pages 154-170.
    7. 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.
    8. 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.
    9. 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.
    10. Bouchhima, Nejmeddine & Schnierle, Marc & Schulte, Sascha & Birke, Kai Peter, 2017. "Optimal energy management strategy for self-reconfigurable batteries," Energy, Elsevier, vol. 122(C), pages 560-569.
    11. Fan, Likang & Wang, Yufei & Wei, Hongqian & Zhang, Youtong & Zheng, Pengyu & Huang, Tianyi & Li, Wei, 2022. "A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 241(C).
    12. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei, 2017. "Particle swarm optimization of driving torque demand decision based on fuel economy for plug-in hybrid electric vehicle," Energy, Elsevier, vol. 123(C), pages 89-107.
    13. Ahmed M. Ali & Dirk Söffker, 2018. "Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions," Energies, MDPI, vol. 11(3), pages 1-24, February.
    14. Tian, He & Li, Shengbo Eben & Wang, Xu & Huang, Yong & Tian, Guangyu, 2018. "Data-driven hierarchical control for online energy management of plug-in hybrid electric city bus," Energy, Elsevier, vol. 142(C), pages 55-67.
    15. Huang, Yanjun & Fard, Soheil Mohagheghi & Khazraee, Milad & Wang, Hong & Khajepour, Amir, 2017. "An adaptive model predictive controller for a novel battery-powered anti-idling system of service vehicles," Energy, Elsevier, vol. 127(C), pages 318-327.
    16. Huang, Yanjun & Wang, Hong & Khajepour, Amir & Li, Bin & Ji, Jie & Zhao, Kegang & Hu, Chuan, 2018. "A review of power management strategies and component sizing methods for hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 132-144.
    17. Hu, Xiaosong & Zou, Yuan & Yang, Yalian, 2016. "Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization," Energy, Elsevier, vol. 111(C), pages 971-980.
    18. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).
    19. Farmann, Alexander & Waag, Wladislaw & Sauer, Dirk Uwe, 2016. "Application-specific electrical characterization of high power batteries with lithium titanate anodes for electric vehicles," Energy, Elsevier, vol. 112(C), pages 294-306.
    20. Ku, Donggyun & Choi, Minje & Yoo, Nakyoung & Shin, Seungheon & Lee, Seungjae, 2021. "A new algorithm for eco-friendly path guidance focused on electric vehicles," Energy, Elsevier, vol. 233(C).

    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:127:y:2017:i:c:p:650-659. 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.