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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
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    References listed on IDEAS

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    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.
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