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Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model.

Muhammad Umair Ali1, Karam Dad Kallu2, Haris Masood3

  • 1Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, South Korea.

Iscience
|November 12, 2021
PubMed
Summary

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A battery is a galvanic cell that is used as a source of electrical power for specific applications. Modern batteries exist in a multitude of forms to accommodate various applications, from tiny button batteries such as those that power wristwatches to the very large batteries used to supply backup energy to municipal power grids. Some batteries are designed for single-use applications and cannot be recharged (primary cells), while others are based on conveniently reversible cell reactions that...
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This study introduces a novel data-driven method to predict lithium-ion battery (LIB) future capacity. The approach combines empirical mode decomposition (EMD) and long short-term memory (LSTM) for highly accurate battery health prognostics.

Area of Science:

  • Battery Technology
  • Data Science
  • Machine Learning

Background:

  • Accurate prediction of lithium-ion battery (LIB) capacity is crucial for reliable energy storage systems.
  • Degradation patterns in LIBs are complex, involving both local regeneration and global trends.
  • Existing prognostics methods often struggle with the intricate nature of battery degradation.

Purpose of the Study:

  • To develop a robust, data-driven approach for predicting the future capacity of lithium-ion batteries.
  • To enhance the accuracy and efficiency of battery health prognostics.
  • To provide a reliable tool for assessing the remaining useful life of LIBs.

Main Methods:

  • Utilized empirical mode decomposition (EMD) to decompose LIB capacity data into intrinsic mode functions (IMFs) and a residual signal.
Keywords:
Computer systems organizationEnergy engineeringEnergy systems

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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  • Employed a kernel recursive least square tracker (KRLST) to track the decomposed IMFs.
  • Applied a long short-term memory (LSTM) sub-model to predict the residual signal.
  • Ensembled the predictions of all IMFs and the residual to forecast future battery capacity.

    • Main Results:

    • The proposed ensemble approach achieved high accuracy in predicting future LIB capacity, with a Root Mean Square Error (RMSE) of 0.00103.
    • Demonstrated significantly lower error rates compared to Gaussian process regression and a standard LSTM fused model.
    • The method showed approximately half the error of other fused models, highlighting its efficiency.

    Conclusions:

    • The developed data-driven ensemble approach effectively predicts future lithium-ion battery capacity.
    • This method offers superior accuracy and efficiency for battery health prognostics compared to existing techniques.
    • The validated high performance makes this approach a valuable tool for battery management and lifecycle assessment.