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Analysis of strategies to maximize the cycle life of lithium-ion batteries based on aging trajectory prediction

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  • Lv, Haichao
  • Kang, Lixia
  • Liu, Yongzhong

Abstract

The capacity fade is commonly observed during the charging and discharging processes of lithium-ion batteries (LIBs). This aging process can be chronologically divided into the linear aging stage and the accelerated aging one by the turning point that is an essential factor affecting the performances and cycle life of LIBs. In this work, to develop a quick and accurate method to quantitatively identify the turning point of LIBs for distinguishing eligible application scenarios, two-stage surrogate models that couple the artificial neural network and nonlinear autoregressive exogenous models were established to predict the turning point, end of life (EOL) and aging trajectory of LIBs based on the two-stage electrochemical model. The operational parameters affecting the knee point and the relationship between the knee point and the EOL of LIBs were analyzed for three application scenarios, which are batteries only used as power batteries, batteries only used for energy storage and batteries sequentially used for power batteries and energy storage systems. The genetic algorithm (GA) was adopted to optimize the combination of operational parameters to maximize the cycle life of LIBs under the three application scenarios. The results show that the proposed model and method can effectively predict the cycle number at the knee point and two-stage aging trajectory of the LIBs. The optimal operating parameters of the LIBs can be determined on the basis of the specific application scenario, optimization objective, and cycle life. The proposed method provides a guidance and tool for optimal utilization of LIBs in multiple stages and optimal design of battery management systems.

Suggested Citation

  • Lv, Haichao & Kang, Lixia & Liu, Yongzhong, 2023. "Analysis of strategies to maximize the cycle life of lithium-ion batteries based on aging trajectory prediction," Energy, Elsevier, vol. 275(C).
    • Handle: RePEc:eee:energy:v:275:y:2023:i:c:s0360544223008472
    DOI: 10.1016/j.energy.2023.127453
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    2. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Aging abnormality detection of lithium-ion batteries combining feature engineering and deep learning," Energy, Elsevier, vol. 297(C).

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