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Optimal parameters estimation of lithium-ion battery in smart grid applications based on gazelle optimization algorithm

Author

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  • Hasanien, Hany M.
  • Alsaleh, Ibrahim
  • Tostado-Véliz, Marcos
  • Alassaf, Abdullah
  • Alateeq, Ayoob
  • Jurado, Francisco

Abstract

The principal contribution of this article focusing on obtaining a precise model of the lithium-ion battery (LiB). This in fact affects the simulation analyses and dynamics of such batteries in several applications including electric vehicles, microgrids, distribution systems, and smart grids. The main challenge here is the heavy nonlinearity of the optimization problem. The proposed gazelle optimization algorithm (GOA) is utilized in minimizing the fitness function, which depends on the integral squared error approach. The error is considered between the identified and practical battery voltage. The validity of the proposed approach is checked considering various operating conditions such as the loading effect, fading impact, and other dynamic responses. The effectiveness of introduced approach is validated by comparing the simulation results with practical results on a real Panasonic LiB of 2.6 Ahr capacity. These results are performed by MATLAB software. Furthermore, GOA-based LiB model is compared with various heuristic and conventional algorithms-based models. With the proposed GOA-based LiB model, an efficient and accurate battery model can be built.

Suggested Citation

  • Hasanien, Hany M. & Alsaleh, Ibrahim & Tostado-Véliz, Marcos & Alassaf, Abdullah & Alateeq, Ayoob & Jurado, Francisco, 2023. "Optimal parameters estimation of lithium-ion battery in smart grid applications based on gazelle optimization algorithm," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223029031
    DOI: 10.1016/j.energy.2023.129509
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    Cited by:

    1. Fahmy, Hend M. & Alqahtani, Ayedh H. & Hasanien, Hany M., 2024. "Precise modeling of lithium-ion battery in industrial applications using Walrus optimization algorithm," Energy, Elsevier, vol. 294(C).

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