IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i6p1548-d514994.html
   My bibliography  Save this article

An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles

Author

Listed:
  • Antonio Galvagno

    (Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy)

  • Umberto Previti

    (Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy)

  • Fabio Famoso

    (Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy)

  • Sebastian Brusca

    (Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy)

Abstract

The most efficient energy management strategies for hybrid vehicles are the “Optimization-Based Strategies”. These strategies require a preliminary knowledge of the driving cycle, which is not easy to predict. This paper aims to combine Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC) low section short trips with real traffic levels for vehicle energy and fuel consumption prediction. Future research can focus on implementing a new strategy for Hybrid Electric Vehicle (HEV) energy optimization, taking into account WLTC and Google Maps traffic levels. First of all, eight characteristic parameters are extracted from real speed profiles, driven in urban road sections in the city of Messina at different traffic conditions, and WLTC short trips as well. The minimum distance algorithm is used to compare the parameters and assign the three traffic levels (heavy, average, and low traffic level) to the WLTC short trips. In this way, for each route assigned from Google maps, vehicle’s energy and fuel consumption are estimated using WLTC short trips remodulated with distances and traffic levels. Moreover, a vehicle numerical model was implemented and used to test the accuracy of fuel consumption and energy prediction for the proposed methodology. The results are promising since the average of the percentage errors’ absolute value between the experimental driving cycles and forecast ones is 3.89% for fuel consumption, increasing to 6.80% for energy.

Suggested Citation

  • Antonio Galvagno & Umberto Previti & Fabio Famoso & Sebastian Brusca, 2021. "An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles," Energies, MDPI, vol. 14(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1548-:d:514994
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/6/1548/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/6/1548/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lu Han & Xiaohong Jiao & Zhao Zhang, 2020. "Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging," Energies, MDPI, vol. 13(1), pages 1-22, January.
    2. Hongwei Liu & Chantong Wang & Xin Zhao & Chong Guo, 2018. "An Adaptive-Equivalent Consumption Minimum Strategy for an Extended-Range Electric Bus Based on Target Driving Cycle Generation," Energies, MDPI, vol. 11(7), pages 1-26, July.
    3. Lincun Fang & Shiyin Qin & Gang Xu & Tianli Li & Kemin Zhu, 2011. "Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms," Energies, MDPI, vol. 4(3), pages 1-13, March.
    4. Hongwen He & Jinquan Guo & Nana Zhou & Chao Sun & Jiankun Peng, 2017. "Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 10(11), pages 1-19, November.
    5. Yavasoglu, H.A. & Tetik, Y.E. & Gokce, K., 2019. "Implementation of machine learning based real time range estimation method without destination knowledge for BEVs," Energy, Elsevier, vol. 172(C), pages 1179-1186.
    6. 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.
    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. Umberto Previti & Sebastian Brusca & Antonio Galvagno & Fabio Famoso, 2022. "Influence of Energy Management System Control Strategies on the Battery State of Health in Hybrid Electric Vehicles," Sustainability, MDPI, vol. 14(19), pages 1-20, 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. 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).
    2. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    3. 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.
    4. Tran, Dai-Duong & Vafaeipour, Majid & El Baghdadi, Mohamed & Barrero, Ricardo & Van Mierlo, Joeri & Hegazy, Omar, 2020. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    5. 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).
    6. 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).
    7. Felipe Jiménez & Wilmar Cabrera-Montiel, 2014. "System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information," Energies, MDPI, vol. 7(6), pages 1-23, June.
    8. Farouk Odeim & Jürgen Roes & Angelika Heinzel, 2015. "Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System," Energies, MDPI, vol. 8(7), pages 1-26, June.
    9. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Zhang, Jiye & Song, Pengyun, 2019. "Modeling and optimal energy management strategy for a catenary-battery-ultracapacitor based hybrid tramway," Energy, Elsevier, vol. 183(C), pages 1123-1135.
    10. Taghavifar, Hadi, 2021. "Fuel cell hybrid range-extender vehicle sizing: Parametric power optimization," Energy, Elsevier, vol. 229(C).
    11. López-Ibarra, Jon Ander & Gaztañaga, Haizea & Saez-de-Ibarra, Andoni & Camblong, Haritza, 2020. "Plug-in hybrid electric buses total cost of ownership optimization at fleet level based on battery aging," Applied Energy, Elsevier, vol. 280(C).
    12. Zhang, Hanyu & Du, Lili, 2023. "Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part I: Modeling and solution algorithm design," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 174-198.
    13. Shi, Wenzhuo & Huangfu, Yigeng & Xu, Liangcai & Pang, Shengzhao, 2022. "Online energy management strategy considering fuel cell fault for multi-stack fuel cell hybrid vehicle based on multi-agent reinforcement learning," Applied Energy, Elsevier, vol. 328(C).
    14. 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).
    15. Rui Xiong & Suleiman M. Sharkh & Xi Zhang, 2018. "Research Progress on Electric and Intelligent Vehicles," Energies, MDPI, vol. 11(7), pages 1-5, July.
    16. 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).
    17. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    18. Jianyun, Zhu & Li, Chen & Lijuan, Xia & Bin, Wang, 2019. "Bi-objective optimal design of plug-in hybrid electric propulsion system for ships," Energy, Elsevier, vol. 177(C), pages 247-261.
    19. Samuel Rodman Oprešnik & Tine Seljak & Rok Vihar & Marko Gerbec & Tomaž Katrašnik, 2018. "Real-World Fuel Consumption, Fuel Cost and Exhaust Emissions of Different Bus Powertrain Technologies," Energies, MDPI, vol. 11(8), pages 1-20, August.
    20. Paweł Krawczyk & Artur Kopczyński & Jakub Lasocki, 2022. "Modeling and Simulation of Extended-Range Electric Vehicle with Control Strategy to Assess Fuel Consumption and CO 2 Emission for the Expected Driving Range," Energies, MDPI, vol. 15(12), pages 1-41, June.

    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:gam:jeners:v:14:y:2021:i:6:p:1548-:d:514994. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.