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Li-ion battery capacity cycling fading dynamics cognition: A stochastic approach

Author

Listed:
  • Lu, Chen
  • Zhang, Lipin
  • Ma, Jian
  • Chen, Zihan
  • Tao, Laifa
  • Su, Yuzhuan
  • Chong, Jin
  • Jin, Haizu
  • Lin, Yongshou

Abstract

Li-ion batteries have been commercially used for many years in small portable devices and electrical vehicles. Here, a five-state nonhomogeneous Markov chain model is introduced, which can assist to saving time and costs incurred by large amounts of cycling life tests with various cell formulations in battery design stage. The model is designed to have five states that belong to three phases: storage phase, active phase, and absorbing phase. The storage phase has one storage state that could be transformed into the third active state; the active phase is comprised of a stable state, an inherent unstable state, and a state that is transformed from the storage phase; the absorbing phase, which is converted from the active phase. The verification results suggest that the proposed model provides an accurate and effective way to cognizing the capacity cycling fading dynamics of various Li-ion batteries with different anode materials even under different working conditions (The cognition accuracy of R-Square can reach 0.999). Furthermore, this method would be a promising way to evaluate the features and performance of Li-ion batteries made of different formulations in the design stage, which could provide valuable information for battery manufacturers to accelerate battery design process.

Suggested Citation

  • Lu, Chen & Zhang, Lipin & Ma, Jian & Chen, Zihan & Tao, Laifa & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2017. "Li-ion battery capacity cycling fading dynamics cognition: A stochastic approach," Energy, Elsevier, vol. 137(C), pages 251-259.
    • Handle: RePEc:eee:energy:v:137:y:2017:i:c:p:251-259
    DOI: 10.1016/j.energy.2017.06.167
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    References listed on IDEAS

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    Cited by:

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    3. Gui, Peng & Deng, Fang & Liang, Zelang & Cai, Yeyun & Chen, Jie, 2018. "Micro linear generator for harvesting mechanical energy from the human gait," Energy, Elsevier, vol. 154(C), pages 365-373.

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