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Alternative utility factor versus the SAE J2841 standard method for PHEV and BEV applications

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  • Paffumi, Elena
  • De Gennaro, Michele
  • Martini, Giorgio

Abstract

This article explores the potential of using real-world driving patterns to derive PHEV and BEV utility factors and evaluates how different travel and recharging behaviours affect the calculation of the standard SAE J2841 utility factor. The study relies on six datasets of driving data collected monitoring 508,607 conventional fuel vehicles in six European areas and a dataset of synthetic data from 700,000 vehicles in a seventh European area. Sources representing the actual driving behaviour of PHEV together with the WLTP European utility factor are adopted as term of comparison. The results show that different datasets of driving data can yield to different estimates of the utility factor. The SAE J2841 standard method results to be representative of a large variety of behaviours of PHEVs and BEVs' drivers, characterised by a fully-charged battery at the beginning of the trip sequence, thus being representative for fuel economy and emission estimates in the early phase deployment of EVs, charged at home and overnight. However the results show that the SAE J2841 utility factor might need to be revised to account for more complex future scenarios, such as necessity-driven recharge behaviour with less than one recharge per day or a fully deployed recharge infrastructure with more than one recharge per day.

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  • Paffumi, Elena & De Gennaro, Michele & Martini, Giorgio, 2018. "Alternative utility factor versus the SAE J2841 standard method for PHEV and BEV applications," Transport Policy, Elsevier, vol. 68(C), pages 80-97.
    • Handle: RePEc:eee:trapol:v:68:y:2018:i:c:p:80-97
    DOI: 10.1016/j.tranpol.2018.02.014
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    References listed on IDEAS

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    2. Xu Hao & Hewu Wang & Minggao Ouyang, 2020. "A novel state-of-charge-based method for plug-in hybrid vehicle electric distance analysis validated with actual driving data," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(3), pages 459-475, March.
    3. Tobias Frambach & Ralf Kleisch & Ralf Liedtke & Jochen Schwarzer & Egbert Figgemeier, 2022. "Environmental Impact Assessment and Classification of 48 V Plug-in Hybrids with Real-Driving Use Case Simulations," Energies, MDPI, vol. 15(7), pages 1-21, March.
    4. Yang, Jue & Chen, Fei, 2020. "How psychological factors related to consumer preferences on plug-in electric passenger vehicles in Chinese cities?A comparison of cities with and without restrictions," MPRA Paper 96165, University Library of Munich, Germany.
    5. Srinivasa Raghavan, Seshadri, 2020. "Behavioral Realism of Plug-In Electric Vehicle Usage: Implications for Emission Benefits, Energy Consumption, and Policies," Institute of Transportation Studies, Working Paper Series qt1rz000pf, Institute of Transportation Studies, UC Davis.
    6. 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.

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