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Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm

Author

Listed:
  • Ragab El-Sehiemy

    (Department of Electrical Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt)

  • Mohamed A. Hamida

    (Ecole Centrale de Nantes, LS2N UMR CNRS, 6004 Nantes, France)

  • Ehab Elattar

    (Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Abdullah Shaheen

    (Department of Electrical Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt)

  • Ahmed Ginidi

    (Department of Electrical Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt)

Abstract

The parameter extraction of parameters for Li-ion batteries is regarded as a critical topic for assessing the performance of battery energy storage systems (BESSs). The supply–demand algorithm (SDA) is used in this work to identify a storage system’s unknown parameters. The parameter-extracting procedure is represented as a nonlinear optimization task in which the state of charge (SOC) is approximated using nonlinear features related to the battery current and the initial SOC condition. Furthermore, the open-circuit voltage is approximated using the resulting SOC, which is performed in a nonlinear formula, as well. When used in the dynamic nonlinear BESS model, the SDA was used to verify the fitness values and standard deviation error. Furthermore, the results that were acquired using SDA are compared to recently developed approaches, which are the gradient-based, tuna swarm, jellyfish, heap-based, and forensic-based optimizers. Simulated studies were paired with experiments for the 40 Ah Kokam Li-ion battery and the ARTEMIS driving-cycle pattern. The numerical outcomes showed that the proposed SDA is an approach which is excellent at identifying the parameters. Furthermore, when compared to the other current optimization techniques, for both the Kokam Li-ion batteries and the ARTEMIS drive-cycle pattern, the suggested SDA exhibited substantial precision.

Suggested Citation

  • Ragab El-Sehiemy & Mohamed A. Hamida & Ehab Elattar & Abdullah Shaheen & Ahmed Ginidi, 2022. "Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm," Energies, MDPI, vol. 15(13), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4556-:d:844984
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    References listed on IDEAS

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    1. Ibrahim M. Safwat & Weilin Li & Xiaohua Wu, 2017. "A Novel Methodology for Estimating State-Of-Charge of Li-Ion Batteries Using Advanced Parameters Estimation," Energies, MDPI, vol. 10(11), pages 1-16, November.
    2. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    3. Liddle, Brantley & Sadorsky, Perry, 2017. "How much does increasing non-fossil fuels in electricity generation reduce carbon dioxide emissions?," Applied Energy, Elsevier, vol. 197(C), pages 212-221.
    4. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
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    Citations

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    Cited by:

    1. Slim Abid & Ali M. El-Rifaie & Mostafa Elshahed & Ahmed R. Ginidi & Abdullah M. Shaheen & Ghareeb Moustafa & Mohamed A. Tolba, 2023. "Development of Slime Mold Optimizer with Application for Tuning Cascaded PD-PI Controller to Enhance Frequency Stability in Power Systems," Mathematics, MDPI, vol. 11(8), pages 1-32, April.
    2. Araby Mahdy & Abdullah Shaheen & Ragab El-Sehiemy & Ahmed Ginidi & Saad F. Al-Gahtani, 2023. "Single- and Multi-Objective Optimization Frameworks of Shape Design of Tubular Linear Synchronous Motor," Energies, MDPI, vol. 16(5), pages 1-27, March.
    3. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    4. Ran Li & Hui Sun & Xue Wei & Weiwen Ta & Haiying Wang, 2022. "Lithium Battery State-of-Charge Estimation Based on AdaBoost.Rt-RNN," Energies, MDPI, vol. 15(16), pages 1-15, August.
    5. Abdullah M. Shaheen & Ragab A. El-Sehiemy & Ahmed Ginidi & Abdallah M. Elsayed & Saad F. Al-Gahtani, 2023. "Optimal Allocation of PV-STATCOM Devices in Distribution Systems for Energy Losses Minimization and Voltage Profile Improvement via Hunter-Prey-Based Algorithm," Energies, MDPI, vol. 16(6), pages 1-20, March.
    6. Asmaa I. Abdelfattah & Mostafa F. Shaaban & Ahmed H. Osman & Abdelfatah Ali, 2023. "Optimal Management of Seasonal Pumped Hydro Storage System for Peak Shaving," Sustainability, MDPI, vol. 15(15), pages 1-23, August.
    7. Hegazy Rezk & A. G. Olabi & Tabbi Wilberforce & Enas Taha Sayed, 2023. "A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    8. Ivan Radaš & Nicole Pilat & Daren Gnjatović & Viktor Šunde & Željko Ban, 2022. "Estimating the State of Charge of Lithium-Ion Batteries Based on the Transfer Function of the Voltage Response to the Current Pulse," Energies, MDPI, vol. 15(18), pages 1-14, September.

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