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Integrated modeling for the cyclic behavior of high power Li-ion batteries under extended operating conditions

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  • Miranda, Á.G.
  • Hong, C.W.

Abstract

The dynamic thermal and electrical behavior of high power LiFePO4 cathode-type Li-ion batteries is studied with extended considerations such as demanded current ranging from 12 to 30A, battery temperatures ranging from 283 to 313K and a redefinition of the concept of state of charge during cycling conditions. The equivalent electrical model, consisting of a series resistance, a parallel resistance–capacitor, a voltage source and state of charge calculators, can be improved with the addition of current and temperature gains for each element. In addition, a non-intrusively-obtained alternative thermal model extraction is proposed to uncouple from the experimental battery temperature based on electrochemical research found in the literature. This improved model extraction for high power cylindrical batteries can achieve a temperature and voltage relative runtime error in the range of 1% and 5% in average, respectively. The effects of lithium concentration in the anode and cathode are accurately predicted with state of charge accelerators, which vary linearly with temperature. Aiming for a power systems environment, the integrated battery model is built and validated experimentally to demonstrate its accurate prediction. This improved integrated battery model can be employed for battery stack simulations, improved state of charge algorithm testing and optimization of hybrid systems - with a light computational demand. Finally, a performance index radar plot is proposed to conveniently compare electrical and thermal properties of different types of batteries.

Suggested Citation

  • Miranda, Á.G. & Hong, C.W., 2013. "Integrated modeling for the cyclic behavior of high power Li-ion batteries under extended operating conditions," Applied Energy, Elsevier, vol. 111(C), pages 681-689.
    • Handle: RePEc:eee:appene:v:111:y:2013:i:c:p:681-689
    DOI: 10.1016/j.apenergy.2013.05.047
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    2. Farmann, Alexander & Sauer, Dirk Uwe, 2018. "Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 225(C), pages 1102-1122.
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    4. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    5. Liu, Guangming & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Hua, Jianfeng, 2015. "A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications," Applied Energy, Elsevier, vol. 149(C), pages 297-314.
    6. Mesbahi, Tedjani & Ouari, Ahmed & Ghennam, Tarak & Berkouk, El Madjid & Rizoug, Nassim & Mesbahi, Nadhir & Meradji, Moudrik, 2014. "A stand-alone wind power supply with a Li-ion battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 204-213.
    7. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy," Applied Energy, Elsevier, vol. 137(C), pages 427-434.
    8. Tanaka, T. & Ito, S. & Muramatsu, M. & Yamada, T. & Kamiko, H. & Kakimoto, N. & Inui, Y., 2015. "Accurate and versatile simulation of transient voltage profile of lithium-ion secondary battery employing internal equivalent electric circuit," Applied Energy, Elsevier, vol. 143(C), pages 200-210.
    9. Miranda, D. & Costa, C.M. & Almeida, A.M. & Lanceros-Méndez, S., 2016. "Computer simulations of the influence of geometry in the performance of conventional and unconventional lithium-ion batteries," Applied Energy, Elsevier, vol. 165(C), pages 318-328.
    10. Wang, Tao & Tseng, K.J. & Zhao, Jiyun & Wei, Zhongbao, 2014. "Thermal investigation of lithium-ion battery module with different cell arrangement structures and forced air-cooling strategies," Applied Energy, Elsevier, vol. 134(C), pages 229-238.
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    12. Bai, Guangxing & Wang, Pingfeng & Hu, Chao & Pecht, Michael, 2014. "A generic model-free approach for lithium-ion battery health management," Applied Energy, Elsevier, vol. 135(C), pages 247-260.

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