IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i23p8032-d692831.html
   My bibliography  Save this article

Real Drive Truth Test of the Toyota Yaris Hybrid 2020 and Energy Analysis Comparison with the 2017 Model

Author

Listed:
  • Fabio Orecchini

    (CARe—Center for Automotive Research and Evolution, DSI—Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy)

  • Adriano Santiangeli

    (CARe—Center for Automotive Research and Evolution, DSI—Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy)

  • Fabrizio Zuccari

    (CARe—Center for Automotive Research and Evolution, DSI—Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy)

  • Adriano Alessandrini

    (Department of Civil and Environmental Engineering, University of Florence, 50121 Florence, Italy)

  • Fabio Cignini

    (ENEA—Italian Agency for New Technologies, Energy and Sustainable Economic Development, 00123 Rome, Italy)

  • Fernando Ortenzi

    (ENEA—Italian Agency for New Technologies, Energy and Sustainable Economic Development, 00123 Rome, Italy)

Abstract

This paper presents the performance analysis of a latest-generation hybrid vehicle (Toyota Yaris 2020) with a testing campaign in real road conditions and a comparison with the previous model (Toyota Yaris 2017). The study was conducted by applying the Real Drive Truth Test protocol, developed by the research group, validated and spread to other full hybrid vehicles: Toyota Prius IV (2016) and Toyota Yaris 2017 (2017). In the case of the 2020 tests, the co-presence on board—deemed unsafe in the usual ways given the ongoing pandemic—was achieved through precise and sophisticated remote control. An on-board diagnostic computer, video transmission and recording equipment guarantee the virtual co-presence of a technical control room and a driver. Thus, several engineers can follow and monitor each vehicle via a 4G modem (installed in each vehicle), analysing data, route and driver behaviour in real-time, and therefore even in the presence of a single occupant in the car under test. The utmost attention has also been paid to adopting anti-COVID behaviours and safety standards: limited personal interactions, reduced co-presence in shared rooms (especially in the control room), vehicle sanitising between different drivers, computers and technicians and video technicians working once at a time. The comparison between the two subsequent vehicle models shows a significant improvement in the performance of the new generation Yaris, both in terms of operation in ZEV (zero-emission vehicle) mode (+15.3%) and in terms of consumption (−35.1%) and overall efficiency of the hybrid powertrain (+8.2%).

Suggested Citation

  • Fabio Orecchini & Adriano Santiangeli & Fabrizio Zuccari & Adriano Alessandrini & Fabio Cignini & Fernando Ortenzi, 2021. "Real Drive Truth Test of the Toyota Yaris Hybrid 2020 and Energy Analysis Comparison with the 2017 Model," Energies, MDPI, vol. 14(23), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8032-:d:692831
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/23/8032/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/23/8032/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fabio Orecchini & Adriano Santiangeli & Fabrizio Zuccari, 2020. "Real Drive Well-to-Wheel Energy Analysis of Conventional and Electrified Car Powertrains," Energies, MDPI, vol. 13(18), pages 1-21, September.
    2. Claudio Cubito & Federico Millo & Giulio Boccardo & Giuseppe Di Pierro & Biagio Ciuffo & Georgios Fontaras & Simone Serra & Marcos Otura Garcia & Germana Trentadue, 2017. "Impact of Different Driving Cycles and Operating Conditions on CO 2 Emissions and Energy Management Strategies of a Euro-6 Hybrid Electric Vehicle," Energies, MDPI, vol. 10(10), pages 1-18, October.
    3. Meinrenken, Christoph J. & Lackner, Klaus S., 2015. "Fleet view of electrified transportation reveals smaller potential to reduce GHG emissions," Applied Energy, Elsevier, vol. 138(C), pages 393-403.
    4. Correa, G. & Muñoz, P.M. & Rodriguez, C.R., 2019. "A comparative energy and environmental analysis of a diesel, hybrid, hydrogen and electric urban bus," Energy, Elsevier, vol. 187(C).
    5. Capasso, Clemente & Veneri, Ottorino, 2014. "Experimental analysis on the performance of lithium based batteries for road full electric and hybrid vehicles," Applied Energy, Elsevier, vol. 136(C), pages 921-930.
    6. Andrzej Szałek & Ireneusz Pielecha, 2021. "The Influence of Engine Downsizing in Hybrid Powertrains on the Energy Flow Indicators under Actual Traffic Conditions," Energies, MDPI, vol. 14(10), pages 1-12, May.
    7. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    Full references (including those not matched with items on IDEAS)

    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. Youssef Amry & Elhoussin Elbouchikhi & Franck Le Gall & Mounir Ghogho & Soumia El Hani, 2022. "Electric Vehicle Traction Drives and Charging Station Power Electronics: Current Status and Challenges," Energies, MDPI, vol. 15(16), pages 1-30, August.
    2. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    4. Xinglong Liu & Fuquan Zhao & Han Hao & Kangda Chen & Zongwei Liu & Hassan Babiker & Amer Ahmad Amer, 2020. "From NEDC to WLTP: Effect on the Energy Consumption, NEV Credits, and Subsidies Policies of PHEV in the Chinese Market," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    5. Da Wang & Chuanxue Song & Yulong Shao & Shixin Song & Silun Peng & Feng Xiao, 2018. "Optimal Control Strategy for Series Hybrid Electric Vehicles in the Warm-Up Process," Energies, MDPI, vol. 11(5), pages 1-20, April.
    6. Pei Zhang & Wangda Lu & Changqing Du & Jie Hu & Fuwu Yan, 2024. "A Comparative Study of Vehicle Velocity Prediction for Hybrid Electric Vehicles Based on a Neural Network," Mathematics, MDPI, vol. 12(4), pages 1-26, February.
    7. Hou, Jun & Song, Ziyou & Park, Hyeongjun & Hofmann, Heath & Sun, Jing, 2018. "Implementation and evaluation of real-time model predictive control for load fluctuations mitigation in all-electric ship propulsion systems," Applied Energy, Elsevier, vol. 230(C), pages 62-77.
    8. 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).
    9. Xixue Liu & Datong Qin & Shaoqian Wang, 2019. "Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor," Energies, MDPI, vol. 12(11), pages 1-17, May.
    10. Zvonimir Dabčević & Branimir Škugor & Jakov Topić & Joško Deur, 2022. "Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology," Energies, MDPI, vol. 15(11), pages 1-21, June.
    11. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    12. Massimiliano Passalacqua & Mauro Carpita & Serge Gavin & Mario Marchesoni & Matteo Repetto & Luis Vaccaro & Sébastien Wasterlain, 2019. "Supercapacitor Storage Sizing Analysis for a Series Hybrid Vehicle," Energies, MDPI, vol. 12(9), pages 1-15, May.
    13. Gnap Jozef & Dočkalik Marek & Dydkowski Grzegorz, 2021. "Examination of the Development of New Bus Registrations with Alternative Powertrains in Europe," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 12(1), pages 147-158, January.
    14. Zheng, Menglian & Wang, Xinhao & Meinrenken, Christoph J. & Ding, Yi, 2018. "Economic and environmental benefits of coordinating dispatch among distributed electricity storage," Applied Energy, Elsevier, vol. 210(C), pages 842-855.
    15. Minhwan Seo & Taedong Goh & Minjun Park & Sang Woo Kim, 2018. "Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell," Energies, MDPI, vol. 11(7), pages 1-18, June.
    16. Sistig, Hubert Maximilian & Sauer, Dirk Uwe, 2023. "Metaheuristic for the integrated electric vehicle and crew scheduling problem," Applied Energy, Elsevier, vol. 339(C).
    17. Wang, An & Tu, Ran & Gai, Yijun & Pereira, Lucas G. & Vaughan, J. & Posen, I. Daniel & Miller, Eric J. & Hatzopoulou, Marianne, 2020. "Capturing uncertainty in emission estimates related to vehicle electrification and implications for metropolitan greenhouse gas emission inventories," Applied Energy, Elsevier, vol. 265(C).
    18. Planakis, Nikolaos & Papalambrou, George & Kyrtatos, Nikolaos, 2022. "Ship energy management system development and experimental evaluation utilizing marine loading cycles based on machine learning techniques," Applied Energy, Elsevier, vol. 307(C).
    19. Wu, Xiaomei & Feng, Qijin & Bai, Chenchen & Lai, Chun Sing & Jia, Youwei & Lai, Loi Lei, 2021. "A novel fast-charging stations locational planning model for electric bus transit system," Energy, Elsevier, vol. 224(C).
    20. Xiong, Siqin & Wang, Yunshi & Bai, Bo & Ma, Xiaoming, 2021. "A hybrid life cycle assessment of the large-scale application of electric vehicles," Energy, Elsevier, vol. 216(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:gam:jeners:v:14:y:2021:i:23:p:8032-:d:692831. 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.