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Relaxation effect analysis on the initial state of charge for LiNi0.5Co0.2Mn0.3O2/graphite battery

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  • Yanhui, Zhang
  • Wenji, Song
  • Guoqing, Xu

Abstract

A model, applicable to a range of the SOC0 (initial state of charge) with a strong interval discharge, is developed and studied based on the traditional OCV (open circuit voltage) model and as a function of parameters of OCV, resting time, current rate and the temperature. Firstly, an online estimation of its OCV is explored based on the LiNi0.5Co0.2Mn0.3O2/graphite battery's intrinsic relationship between the SOC and the OCV according to the experimental data. Then, an equivalent circuit model with two series RC networks is adopted modeling the polarization characteristic and the dynamic behavior of the LiNi0.5Co0.2Mn0.3O2/graphiten battery, the corresponding dynamic electric behavior is built with equations. An resting time estimation method is put forward theoretically and is verified experimentally, where the resting time is updated with the real-time measurements of battery parameter of model at each sampling interval, in order to perfect overcome the drawback of OCV model for SOC0 estimate. What is more, the effect of multiple temperatures to terminal voltage are considered. It makes contributions to the existing literature. Finally, a verifying experiment is carried out based on DST (Dynamic Stress Test) to evaluate the performance and robustness of the proposed SOC0.

Suggested Citation

  • Yanhui, Zhang & Wenji, Song & Guoqing, Xu, 2014. "Relaxation effect analysis on the initial state of charge for LiNi0.5Co0.2Mn0.3O2/graphite battery," Energy, Elsevier, vol. 74(C), pages 368-373.
    • Handle: RePEc:eee:energy:v:74:y:2014:i:c:p:368-373
    DOI: 10.1016/j.energy.2014.06.105
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    References listed on IDEAS

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    Cited by:

    1. Farmann, Alexander & Waag, Wladislaw & Sauer, Dirk Uwe, 2016. "Application-specific electrical characterization of high power batteries with lithium titanate anodes for electric vehicles," Energy, Elsevier, vol. 112(C), pages 294-306.
    2. Dai, Haifeng & Guo, Pingjing & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan, 2015. "ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries," Energy, Elsevier, vol. 80(C), pages 350-360.
    3. Ouyang, Tiancheng & Xu, Peihang & Chen, Jingxian & Su, Zixiang & Huang, Guicong & Chen, Nan, 2021. "A novel state of charge estimation method for lithium-ion batteries based on bias compensation," Energy, Elsevier, vol. 226(C).

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