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Framework for energy storage selection to design the next generation of electrified military vehicles

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  • Catenaro, Edoardo
  • Rizzo, Denise M.
  • Onori, Simona

Abstract

In this paper, a methodology is proposed that aims at selecting the most suitable energy storage system (ESS) for a targeted application. Specifically, the focus is on electrified military vehicles for the wide range of load requirements, driving missions and operating conditions call for such a cohesive framework. The method uses the Enhanced-Ragone plot (ERp) as a guiding tool to map the performance of different lithium-ion batteries, as a function of C-rate and temperature, and supercapacitors, on the specific power and specific energy log-log plane. A frequency-based segmentation strategy is employed to assign the requested power to the powertrain actuators. Both full-electric battery-powered and hybrid electric vehicle (including an internal combustion engine, battery and supercapacitors) configurations are considered. Using the ERp, ESSs that are able to match the C-rate corresponding to the power-to-energy ratio calculated from the load are selected. Moreover, weight, volume, number of cells and pack energy of the selected ESSs are also returned from the design framework. The algorithm is tested over three vehicle powertrains which strongly differ in load requirements - Tesla Model S, Tesla Semi truck and high-mobility multipurpose wheeled vehicle.

Suggested Citation

  • Catenaro, Edoardo & Rizzo, Denise M. & Onori, Simona, 2021. "Framework for energy storage selection to design the next generation of electrified military vehicles," Energy, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:energy:v:231:y:2021:i:c:s0360544221009439
    DOI: 10.1016/j.energy.2021.120695
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    References listed on IDEAS

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    1. Catenaro, Edoardo & Rizzo, Denise M. & Onori, Simona, 2021. "Experimental analysis and analytical modeling of Enhanced-Ragone plot," Applied Energy, Elsevier, vol. 291(C).
    2. Ankur Bhattacharjee & Rakesh K. Mohanty & Aritra Ghosh, 2020. "Design of an Optimized Thermal Management System for Li-Ion Batteries under Different Discharging Conditions," Energies, MDPI, vol. 13(21), pages 1-21, October.
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    Cited by:

    1. Khan, F.M. NizamUddin & Rasul, Mohammad G. & Sayem, A.S.M. & Mandal, Nirmal K., 2024. "A computational analysis of effects of electrode thickness on the energy density of lithium-ion batteries," Energy, Elsevier, vol. 288(C).

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