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A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures

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  • Wang, Chun
  • He, Hongwen
  • Zhang, Yongzhi
  • Mu, Hao

Abstract

This paper presents a systematic evaluation for five typical equivalent circuit models (ECMs) of ultracapacitors (UCs) under different ambient temperatures. A comprehensive experimental profile is designed to obtain the test datasets. The genetic algorithm (GA) is employed to identify the model parameters for five UC models under different temperatures and state of charge (SOCs). Three results can be obtained from the systematic analysis. (1) Due to the better model accuracy and robustness, the Thevenin model is preferred for UC cell modeling with the maximum errors less than 8mV. (2) Compared with the other four UC models, the Thevenin model with one-state hysteresis (Thevenin-hys model) is preferred for UC pack modeling because of its better performance. (3) From the point of view of model accuracy and robustness against different ambient temperatures, if the SOC is less than 0.5, the UCs are not suitable for further application.

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  • Wang, Chun & He, Hongwen & Zhang, Yongzhi & Mu, Hao, 2017. "A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures," Applied Energy, Elsevier, vol. 196(C), pages 268-278.
    • Handle: RePEc:eee:appene:v:196:y:2017:i:c:p:268-278
    DOI: 10.1016/j.apenergy.2017.03.060
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    2. Bizhong Xia & Zheng Zhang & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 11(6), pages 1-20, June.
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    4. Macias, A. & Kandidayeni, M. & Boulon, L. & Trovão, J.P., 2021. "Fuel cell-supercapacitor topologies benchmark for a three-wheel electric vehicle powertrain," Energy, Elsevier, vol. 224(C).
    5. 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.
    6. Reddi Khasim, Shaik & Dhanamjayulu, C., 2021. "Selection parameters and synthesis of multi-input converters for electric vehicles: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    7. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    8. Wang, Chun & Yang, Ruixin & Yu, Quanqing, 2019. "Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty," Applied Energy, Elsevier, vol. 256(C).
    9. Wei Zhang & Jixin Wang & Shaofeng Du & Hongfeng Ma & Wenjun Zhao & Haojie Li, 2019. "Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends," Energies, MDPI, vol. 12(10), pages 1-26, May.
    10. Zheng Chen & Jiapeng Xiao & Xing Shu & Shiquan Shen & Jiangwei Shen & Yonggang Liu, 2020. "Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan," Energies, MDPI, vol. 13(6), pages 1-15, March.
    11. Yanwei Liu & Jiansheng Liang & Jiaqing Song & Jie Ye, 2022. "Research on Energy Management Strategy of Fuel Cell Vehicle Based on Multi-Dimensional Dynamic Programming," Energies, MDPI, vol. 15(14), pages 1-20, July.
    12. Liang Zhang & Shunli Wang & Daniel-Ioan Stroe & Chuanyun Zou & Carlos Fernandez & Chunmei Yu, 2020. "An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries," Energies, MDPI, vol. 13(8), pages 1-12, April.
    13. Shuaishuai Zhang & Youhong Wan & Jie Ding & Yangyang Da, 2021. "State of Charge (SOC) Estimation Based on Extended Exponential Weighted Moving Average H ∞ Filtering," Energies, MDPI, vol. 14(6), pages 1-15, March.
    14. Zhang, Wei & Wang, Jixin & Xu, Zhenyu & Shen, Yuying & Gao, Guangzong, 2022. "A generalized energy management framework for hybrid construction vehicles via model-based reinforcement learning," Energy, Elsevier, vol. 260(C).

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