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Study on the combined influence of battery models and sizing strategy for hybrid and battery-based electric vehicles

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  • Pinto, Cláudio
  • Barreras, Jorge V.
  • de Castro, Ricardo
  • Araújo, Rui Esteves
  • Schaltz, Erik

Abstract

This paper presents a study of the combined influence of battery models and sizing strategy for hybrid and battery-based electric vehicles. In particular, the aim is to find the number of battery (and supercapacitor) cells to propel a light vehicle to run two different standard driving cycles. Three equivalent circuit models are considered to simulate the battery electrical performance: linear static, non-linear static and non-linear with first-order dynamics. When dimensioning a battery-based vehicle, less complex models may lead to a solution with more battery cells and higher costs. Despite the same tendency, when a hybrid vehicle is taken into account, the influence of the battery models is dependent on the sizing strategy. In this work, two sizing strategies are evaluated: dynamic programming and filter-based. For the latter, the complexity of the battery model has a clear influence on the result of the sizing problem. On the other hand, a modest influence is observed when a dynamic programming strategy is followed.

Suggested Citation

  • Pinto, Cláudio & Barreras, Jorge V. & de Castro, Ricardo & Araújo, Rui Esteves & Schaltz, Erik, 2017. "Study on the combined influence of battery models and sizing strategy for hybrid and battery-based electric vehicles," Energy, Elsevier, vol. 137(C), pages 272-284.
    • Handle: RePEc:eee:energy:v:137:y:2017:i:c:p:272-284
    DOI: 10.1016/j.energy.2017.07.009
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    1. Bruno Martin de Alcântara Dias & Cynthia Thamires da Silva & Rui Esteves Araújo & Ricardo de Castro & Eduardo Lorenzetti Pellini & Cláudio Pinto & Armando Antônio Maria Laganá, 2022. "An Analytic Hierarchy Process for Selecting Battery Equalization Methods," Energies, MDPI, vol. 15(7), pages 1-21, March.
    2. Wang, Y. & Qiao, X. & Zhang, C. & Zhou, Xiangyang, 2018. "Self-discharge of a hybrid supercapacitor with incorporated galvanic cell components," Energy, Elsevier, vol. 159(C), pages 1035-1045.
    3. Wu, Zhou & Ling, Rui & Tang, Ruoli, 2017. "Dynamic battery equalization with energy and time efficiency for electric vehicles," Energy, Elsevier, vol. 141(C), pages 937-948.
    4. Xi Luo & Jorge Varela Barreras & Clementine L. Chambon & Billy Wu & Efstratios Batzelis, 2021. "Hybridizing Lead–Acid Batteries with Supercapacitors: A Methodology," Energies, MDPI, vol. 14(2), pages 1-27, January.
    5. Li, Shiying & Xu, Jun & Gao, Haonan & Tao, Tao & Mei, Xuesong, 2020. "Safety probability based multi-objective optimization of energy-harvesting suspension system," Energy, Elsevier, vol. 209(C).
    6. Hou, Shengyan & Yin, Hai & Xu, Fuguo & Benjamín, Pla & Gao, Jinwu & Chen, Hong, 2023. "Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles," Energy, Elsevier, vol. 266(C).
    7. Wu, Jian & Wang, Xiangyu & Li, Liang & Qin, Cun'an & Du, Yongchang, 2018. "Hierarchical control strategy with battery aging consideration for hybrid electric vehicle regenerative braking control," Energy, Elsevier, vol. 145(C), pages 301-312.

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