IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v301y2021ics0306261921008801.html
   My bibliography  Save this article

Impact of the hybrid electric architecture on the performance and emissions of a delivery truck with a dual-fuel RCCI engine

Author

Listed:
  • García, Antonio
  • Monsalve-Serrano, Javier
  • Martinez-Boggio, Santiago
  • Gaillard, Patrick

Abstract

Reactivity controlled compression ignition combustion showed great advantages in terms of NOx and soot emissions reduction, leading to virtually zero emissions. However, the average brake thermal efficiency of this concept is like that found with conventional diesel operation. The powertrain electrification using electric motors and battery packages appears as a potential solution to reduce the CO2 emissions. For this reason, several solutions for the powertrain electrification can be found currently in the market as the parallel, series and power split powertrain architectures. The aim of this work is to evaluate the hybrid architecture impact on the fuel consumption and emissions of a delivery truck (Volvo-FL) intended for urban and urban–rural applications. The truck equipped with a reactivity-controlled compression ignition diesel-gasoline engine is evaluated and compared against the conventional diesel case. In addition, to evaluate the impact of new e-fuels on the well-to-wheel CO2 emissions, a synthetic gasoline coming from carbon capture and green electricity is evaluated. The results show that hybridization allows reducing the tank-to-wheel CO2 emissions above 15% with the parallel hybrid set-up. The series and power split architectures show CO2 benefits of 12% with respect to the baseline diesel non-hybrid case. Using synthetic gasoline as low reactivity fuel allows to achieve a 50% well-to-wheel CO2 reduction in the P2 and 70% well-to-wheel CO2 reduction for the series and power split cases due to the higher average gasoline fraction used in the driving cycle.

Suggested Citation

  • García, Antonio & Monsalve-Serrano, Javier & Martinez-Boggio, Santiago & Gaillard, Patrick, 2021. "Impact of the hybrid electric architecture on the performance and emissions of a delivery truck with a dual-fuel RCCI engine," Applied Energy, Elsevier, vol. 301(C).
  • Handle: RePEc:eee:appene:v:301:y:2021:i:c:s0306261921008801
    DOI: 10.1016/j.apenergy.2021.117494
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921008801
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2021.117494?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. Helgeson, Broghan & Peter, Jakob, 2020. "The role of electricity in decarbonizing European road transport – Development and assessment of an integrated multi-sectoral model," Applied Energy, Elsevier, vol. 262(C).
    2. Guille des Buttes, Alice & Jeanneret, Bruno & Kéromnès, Alan & Le Moyne, Luis & Pélissier, Serge, 2020. "Energy management strategy to reduce pollutant emissions during the catalyst light-off of parallel hybrid vehicles," Applied Energy, Elsevier, vol. 266(C).
    3. Millo, Federico & Rolando, Luciano & Fuso, Rocco & Zhao, Jianning, 2015. "Development of a new hybrid bus for urban public transportation," Applied Energy, Elsevier, vol. 157(C), pages 583-594.
    4. He, Yinglong & Wang, Chongming & Zhou, Quan & Li, Ji & Makridis, Michail & Williams, Huw & Lu, Guoxiang & Xu, Hongming, 2020. "Multiobjective component sizing of a hybrid ethanol-electric vehicle propulsion system," Applied Energy, Elsevier, vol. 266(C).
    5. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).
    6. Zhang, Bo & Zhang, Jiangyan & Xu, Fuguo & Shen, Tielong, 2020. "Optimal control of power-split hybrid electric powertrains with minimization of energy consumption," Applied Energy, Elsevier, vol. 266(C).
    7. Vora, Ashish P. & Jin, Xing & Hoshing, Vaidehi & Saha, Tridib & Shaver, Gregory & Varigonda, Subbarao & Wasynczuk, Oleg & Tyner, Wallace E., 2017. "Design-space exploration of series plug-in hybrid electric vehicles for medium-duty truck applications in a total cost-of-ownership framework," Applied Energy, Elsevier, vol. 202(C), pages 662-672.
    8. Xu, Bin & Rathod, Dhruvang & Zhang, Darui & Yebi, Adamu & Zhang, Xueyu & Li, Xiaoya & Filipi, Zoran, 2020. "Parametric study on reinforcement learning optimized energy management strategy for a hybrid electric vehicle," Applied Energy, Elsevier, vol. 259(C).
    9. Li, Zilong & Zhang, Yaoyuan & Huang, Guan & Zhao, Wenbin & He, Zhuoyao & Qian, Yong & Lu, Xingcai, 2020. "Control of intake boundary conditions for enabling clean combustion in variable engine conditions under intelligent charge compression ignition (ICCI) mode," Applied Energy, Elsevier, vol. 274(C).
    10. Forrest, Kate & Mac Kinnon, Michael & Tarroja, Brian & Samuelsen, Scott, 2020. "Estimating the technical feasibility of fuel cell and battery electric vehicles for the medium and heavy duty sectors in California," Applied Energy, Elsevier, vol. 276(C).
    11. Shiyu Gan & Daniela Chrenko & Alan Kéromnès & Luis Le Moyne, 2018. "Development of a Multi-Architecture and Multi-Application Hybrid Vehicle Design and Management Tool," Energies, MDPI, vol. 11(11), pages 1-19, November.
    12. Li, Yuecheng & He, Hongwen & Khajepour, Amir & Wang, Hong & Peng, Jiankun, 2019. "Energy management for a power-split hybrid electric bus via deep reinforcement learning with terrain information," Applied Energy, Elsevier, vol. 255(C).
    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. Novella, Ricardo & García, Antonio & Gomez-Soriano, Josep & Fogué-Robles, Álvaro, 2023. "Exploring dilution potential for full load operation of medium duty hydrogen engine for the transport sector," Applied Energy, Elsevier, vol. 349(C).
    2. Santiago Martinez-Boggio & Javier Monsalve-Serrano & Antonio García & Pedro Curto-Risso, 2023. "High Degree of Electrification in Heavy-Duty Vehicles," Energies, MDPI, vol. 16(8), pages 1-20, April.
    3. Zhang, Hao & Liu, Shang & Lei, Nuo & Fan, Qinhao & Wang, Zhi, 2022. "Leveraging the benefits of ethanol-fueled advanced combustion and supervisory control optimization in hybrid biofuel-electric vehicles," Applied Energy, Elsevier, vol. 326(C).
    4. Sven Schulze & Günter Feyerl & Stefan Pischinger, 2023. "Advanced ECMS for Hybrid Electric Heavy-Duty Trucks with Predictive Battery Discharge and Adaptive Operating Strategy under Real Driving Conditions," Energies, MDPI, vol. 16(13), pages 1-29, July.
    5. Navid Balazadeh Meresht & Sina Moghadasi & Sandeep Munshi & Mahdi Shahbakhti & Gordon McTaggart-Cowan, 2023. "Advances in Vehicle and Powertrain Efficiency of Long-Haul Commercial Vehicles: A Review," Energies, MDPI, vol. 16(19), pages 1-37, September.

    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. Zhang, Hao & Liu, Shang & Lei, Nuo & Fan, Qinhao & Wang, Zhi, 2022. "Leveraging the benefits of ethanol-fueled advanced combustion and supervisory control optimization in hybrid biofuel-electric vehicles," Applied Energy, Elsevier, vol. 326(C).
    2. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    3. Zhang, Bo & Zhang, Jiangyan & Shen, Tielong, 2022. "Optimal control design for comfortable-driving of hybrid electric vehicles in acceleration mode," Applied Energy, Elsevier, vol. 305(C).
    4. 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).
    5. Anselma, Pier Giuseppe, 2022. "Electrified powertrain sizing for vehicle fleets of car makers considering total ownership costs and CO2 emission legislation scenarios," Applied Energy, Elsevier, vol. 314(C).
    6. García, Antonio & Carlucci, Paolo & Monsalve-Serrano, Javier & Valletta, Andrea & Martínez-Boggio, Santiago, 2021. "Energy management optimization for a power-split hybrid in a dual-mode RCCI-CDC engine," Applied Energy, Elsevier, vol. 302(C).
    7. Yang, Ningkang & Han, Lijin & Xiang, Changle & Liu, Hui & Li, Xunmin, 2021. "An indirect reinforcement learning based real-time energy management strategy via high-order Markov Chain model for a hybrid electric vehicle," Energy, Elsevier, vol. 236(C).
    8. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    9. Yang, Ningkang & Han, Lijin & Bo, Lin & Liu, Baoshuai & Chen, Xiuqi & Liu, Hui & Xiang, Changle, 2023. "Real-time adaptive energy management for off-road hybrid electric vehicles based on decision-time planning," Energy, Elsevier, vol. 282(C).
    10. Chen, Jiaxin & Shu, Hong & Tang, Xiaolin & Liu, Teng & Wang, Weida, 2022. "Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment," Energy, Elsevier, vol. 239(PC).
    11. García, Antonio & Monsalve-Serrano, Javier & Martinez-Boggio, Santiago & Gaillard, Patrick, 2021. "Emissions reduction by using e-components in 48 V mild hybrid trucks under dual-mode dual-fuel combustion," Applied Energy, Elsevier, vol. 299(C).
    12. Yang, Chao & Liu, Kaijia & Jiao, Xiaohong & Wang, Weida & Chen, Ruihu & You, Sixiong, 2022. "An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 239(PB).
    13. Matteo Prussi & Lorenzo Laveneziana & Lorenzo Testa & David Chiaramonti, 2022. "Comparing e-Fuels and Electrification for Decarbonization of Heavy-Duty Transports," Energies, MDPI, vol. 15(21), pages 1-17, October.
    14. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
    15. García, Antonio & Carlucci, Paolo & Monsalve-Serrano, Javier & Valletta, Andrea & Martínez-Boggio, Santiago, 2020. "Energy management strategies comparison for a parallel full hybrid electric vehicle using Reactivity Controlled Compression Ignition combustion," Applied Energy, Elsevier, vol. 272(C).
    16. Serrano, José Ramón & García, Antonio & Monsalve-Serrano, Javier & Martínez-Boggio, Santiago, 2021. "High efficiency two stroke opposed piston engine for plug-in hybrid electric vehicle applications: Evaluation under homologation and real driving conditions," Applied Energy, Elsevier, vol. 282(PA).
    17. Wu, Yitao & Zhang, Yuanjian & Li, Guang & Shen, Jiangwei & Chen, Zheng & Liu, Yonggang, 2020. "A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks," Energy, Elsevier, vol. 208(C).
    18. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    19. Zhang, Hao & Fan, Qinhao & Liu, Shang & Li, Shengbo Eben & Huang, Jin & Wang, Zhi, 2021. "Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine," Applied Energy, Elsevier, vol. 304(C).
    20. Zhang, Wei & Wang, Jixin & Xu, Zhenyu & Shen, Yuying & Gao, Guangzong, 2022. "A generalized energy management framework for hybrid construction vehicles via model-based reinforcement learning," Energy, Elsevier, vol. 260(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:appene:v:301:y:2021:i:c:s0306261921008801. 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.