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Sizing a battery-supercapacitor energy storage system with battery degradation consideration for high-performance electric vehicles

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  • Zhu, Tao
  • Lot, Roberto
  • Wills, Richard G.A.
  • Yan, Xingda

Abstract

This paper presents sizing guides and energy management (EM) benchmarks of battery-supercapacitor (SC) hybrid energy storage system (HESS) in electric vehicle (EV) applications. We explain how to optimize the HESS size in order to minimize battery degradation and financial costs in EVs. We also illustrate the optimal EM benchmarks that can minimize battery degradation with whatever EM technique implemented. By decoupling the EM problem from the sizing one, we reveal the general trends of battery degradation with HESS size, which are irrelevant to design parameters of EVs and specifications of batteries and SCs. The vehicle-lifetime battery replacements and HESS costs are discussed with HESS sizing method. The efficacy of the proposed sizing guides and EM benchmarks is tested in the case study of a sports EV. Results show that the optimally sized HESS can extend battery lifetime by 37% as compared with the battery-only energy storage system and can reduce vehicle-lifetime HESS costs by up to 39% as compared with the unoptimized HESS designs, respectively.

Suggested Citation

  • Zhu, Tao & Lot, Roberto & Wills, Richard G.A. & Yan, Xingda, 2020. "Sizing a battery-supercapacitor energy storage system with battery degradation consideration for high-performance electric vehicles," Energy, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:energy:v:208:y:2020:i:c:s0360544220314432
    DOI: 10.1016/j.energy.2020.118336
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    7. Nguyễn, Bảo-Huy & Vo-Duy, Thanh & Henggeler Antunes, Carlos & Trovão, João Pedro F., 2021. "Multi-objective benchmark for energy management of dual-source electric vehicles: An optimal control approach," Energy, Elsevier, vol. 223(C).
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    9. Mohseni, Soheil & Khalid, Roomana & Brent, Alan C., 2023. "Stochastic, resilience-oriented optimal sizing of off-grid microgrids considering EV-charging demand response: An efficiency comparison of state-of-the-art metaheuristics," Applied Energy, Elsevier, vol. 341(C).

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