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Prognostics of battery cycle life in the early-cycle stage based on hybrid model

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  • Zhang, Yu
  • Peng, Zhen
  • Guan, Yong
  • Wu, Lifeng

Abstract

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries in early-cycle stage can speed up the battery improvement and optimization. However, slowly varying and weak predictability of the characteristic quantities in early-cycle stage make it challenging to predict RUL. To overcome this problem, the paper proposes a hybrid prediction model, which integrates random forest (RF), Artificial Bee Colony (ABC) and general regression neural network (GRNN), called RF-ABC-GRNN. First, a new feature space is obtained by performing linear and non-linear transformations on the original features that are slowly changed in early-cycle stage, such as discharge capacity, terminal voltage, discharge current, and internal resistance. Second, RF is used to measure and rank the importance of these new features so as to screen out the high-importance feature combination. Third, the prediction model based on GRNN is constructed. Considering that the smoothing parameter of the model has great influence on the prediction performance, ABC is used for parameter optimization. Finally, in order to verify performance of the model, initial cycles data that have yet to exhibit apparent degradation is used. Comparison results show that the proposed model could effectively screen out the high-importance features and make accurate prediction much earlier.

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  • Zhang, Yu & Peng, Zhen & Guan, Yong & Wu, Lifeng, 2021. "Prognostics of battery cycle life in the early-cycle stage based on hybrid model," Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s036054422100150x
    DOI: 10.1016/j.energy.2021.119901
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