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Design-space exploration of series plug-in hybrid electric vehicles for medium-duty truck applications in a total cost-of-ownership framework

Author

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  • Vora, Ashish P.
  • Jin, Xing
  • Hoshing, Vaidehi
  • Saha, Tridib
  • Shaver, Gregory
  • Varigonda, Subbarao
  • Wasynczuk, Oleg
  • Tyner, Wallace E.

Abstract

The light-duty vehicle market has seen some adoption of hybrid electric vehicles that is not reflected in the heavy-duty market. The major challenges associated with the heavy-duty segment are: (i) greater emphasis on economic viability, (ii) reluctance to take on risk associated with new technologies, and (iii) numerous diverse applications that preclude a one-size-fits-all approach to hybrid-electric powertrain design. To overcome these challenges, a model-based framework is required that enables the exploration and optimal design of powertrain architectures for diverse applications while capturing the impact of hybridization on the economics of ownership under different economic scenarios. This paper demonstrates such a framework that incorporates powertrain simulation and battery degradation models to predict fuel consumption, electrical energy consumption, and battery replacements. These results are combined with economic assumptions to enable the exploration of a large design space (which spans powertrain design & control variables, noise variables, and economic scenarios) from a total cost-of-ownership perspective to provide better insights to vehicle integrators, component manufacturers, and buyers of heavy-duty hybrid electric vehicles. The methodology is applied to series plug-in hybrid electric and extended-range electric powertrain architectures for medium duty truck applications. The results show that under the assumptions made, economically favorable solutions for series plug-in hybrid electric medium-duty trucks exist in the 2020 time-frame for the NY Composite Truck drive cycle, while for the HTUF Refuse Truck and HTUF Class 6 P&D Truck drive cycles, feasible solutions are not obtained until 2025 and 2030 time-frames respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:202:y:2017:i:c:p:662-672
    DOI: 10.1016/j.apenergy.2017.05.090
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    12. Moon, Saedaseul & Lee, Deok-Joo, 2019. "An optimal electric vehicle investment model for consumers using total cost of ownership: A real option approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    13. Palmer, Kate & Tate, James E. & Wadud, Zia & Nellthorp, John, 2018. "Total cost of ownership and market share for hybrid and electric vehicles in the UK, US and Japan," Applied Energy, Elsevier, vol. 209(C), pages 108-119.
    14. 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).
    15. Cipek, Mihael & Kasać, Josip & Pavković, Danijel & Zorc, Davor, 2020. "A novel cascade approach to control variables optimisation for advanced series-parallel hybrid electric vehicle power-train," Applied Energy, Elsevier, vol. 276(C).
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