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A New Hybrid White Shark and Whale Optimization Approach for Estimating the Li-Ion Battery Model Parameters

Author

Listed:
  • Ahmed Fathy

    (Department of Electrical Engineering, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Dalia Yousri

    (Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt)

  • Abdullah G. Alharbi

    (Department of Electrical Engineering, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Mohammad Ali Abdelkareem

    (Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates
    Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah 27272, United Arab Emirates
    Chemical Engineering Department, Minia University, Elminia 61111, Egypt)

Abstract

Constructing a reliable equivalent circuit of Li-Ion batteries using real operating conditions by estimating optimal parameters is mandatory for many engineering applications, as it controls the energy management of the battery in a hybrid system. However, model parameters can vary according to the electrochemical nature of the battery, so improving the accuracy of the battery model parameters is essential to obtain reliable and accurate equivalent circuits. Therefore, this paper proposes a new efficient hybrid optimization approach for determining the proper parameters of Li-ion battery Shepherd model equivalent circuits. The proposed algorithm comprises a white shark optimizer (WSO) and the whale optimization approach (WOA) for modifying the stochastic behavior of the WSO while searching for food sources. Minimizing the root mean square error between the estimated and measured battery voltages is the objective function considered in this work. The hybrid variant of the WSO (HWSO) was examined with two different types of batteries. Moreover, the proposed HWSO was validated versus a set of recent meta-heuristic approaches including the sea horse optimizer (SHO), artificial gorilla troops optimizer (GTO), coyote optimization algorithm (COA), and the basic version of the WSO. Furthermore, statistical analyses, mean convergence, and fitting curves were conducted for the comparisons. The proposed HWSO succeeded in achieving the least fitness values of 2.6172 × 10 −4 and 5.6118 × 10 −5 with standard deviations of 9.3861 × 10 −5 and 3.2854 × 10 −4 for battery 1 and battery 2, respectively. On the other hand, the worst fitness values were 6.5230 × 10 −2 and 6.6197 × 10 −5 via SHO and WSO for both considered batteries. The proposed HWSO results prove the efficiency of the proposed approach in providing highly accurate battery model parameters with high consistency and a unique convergence curve compared to the other methods.

Suggested Citation

  • Ahmed Fathy & Dalia Yousri & Abdullah G. Alharbi & Mohammad Ali Abdelkareem, 2023. "A New Hybrid White Shark and Whale Optimization Approach for Estimating the Li-Ion Battery Model Parameters," Sustainability, MDPI, vol. 15(7), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5667-:d:1105767
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    References listed on IDEAS

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    Cited by:

    1. Fernando Lepe-Silva & Broderick Crawford & Felipe Cisternas-Caneo & José Barrera-Garcia & Ricardo Soto, 2024. "A Binary Chaotic White Shark Optimizer," Mathematics, MDPI, vol. 12(20), pages 1-35, October.
    2. Isen, Evren & Duman, Serhat, 2024. "Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems," Applied Energy, Elsevier, vol. 365(C).

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