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Modified bald eagle search algorithm for lithium-ion battery model parameters extraction.

Seydali Ferahtia1, Hegazy Rezk2, Ali Djerioui1

  • 1Laboratoire de Génie Electrique Department of Electrical Engineering, University of M'sila, Algeria.

ISA Transactions
|September 10, 2022
PubMed
Summary
This summary is machine-generated.

A modified Bald Eagle Search algorithm (mBES) enhances exploration and exploitation by incorporating adaptive parameters. This improved algorithm accurately extracts complex lithium-ion battery parameters, outperforming the original BES.

Keywords:
Bald eagle search algorithm (BES)Lithium-ion battery modelMetaheuristic optimization algorithms (MAs)Parameters identification

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Battery Modeling

Background:

  • Metaheuristic algorithms like the Bald Eagle Search (BES) algorithm can suffer from local optima and slow convergence.
  • Extracting parameters for complex models, such as lithium-ion batteries, presents significant challenges.

Purpose of the Study:

  • To enhance the Bald Eagle Search algorithm (BES) by introducing adaptive parameters to improve its performance.
  • To apply the modified BES (mBES) for accurate parameter extraction in lithium-ion battery models.

Main Methods:

  • Adaptive parameters were integrated into the BES algorithm, adjusting based on the current and maximum number of iterations.
  • The modified BES (mBES) was evaluated on benchmark test functions and applied to lithium-ion battery parameter extraction.
  • Statistical tests, including analysis of variance and Tukey tests, were used to validate the results.

Main Results:

  • The mBES demonstrated superior performance on benchmark test functions compared to other recent algorithms.
  • Parameter extraction for a lithium-ion battery using mBES achieved a root mean square error of 0.89 × 10⁻³, outperforming the original BES (1.013 × 10⁻³).
  • Testing on the New European Driving Cycle profile showed mBES achieving the lowest fitness value (0.058896) and standard deviation (5.89 × 10⁻⁷).

Conclusions:

  • The adaptive parameters effectively improved the diversity and convergence of the BES algorithm.
  • The mBES algorithm is a robust and precise tool for complex parameter extraction tasks, particularly for lithium-ion batteries.
  • The enhanced algorithm shows significant potential for applications requiring accurate battery modeling and optimization.