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Precise modeling of lithium-ion battery in industrial applications using Walrus optimization algorithm

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  • Fahmy, Hend M.
  • Alqahtani, Ayedh H.
  • Hasanien, Hany M.

Abstract

This research article demonstrates how to get a precise lithium-ion battery (LIB) model using one of the artificial intelligence algorithms called the Walrus optimization algorithm (WaOA). The model's accuracy affects several transient and dynamic analysis simulations, which are carried out for power systems, electric vehicles, and many transportation applications. The LIB model may have one, two, or three resistance-capacitance (RC) models, signifying the complexity of the optimization challenge. Therefore, the WaOA is used to minimize the cost function that relies on an integral square error criterion. This criterion calculates the error between the estimated and experimental voltages. The proposed method is validated under several conditions, taking into account load variation, battery degradation, temperature fluctuation, and different RC models. The numerical results of the WaOA method are compared with their experimental results for a 2.6 Ah LIB. In addition, the proposed WaOA model has undergone validation alongside numerous optimization algorithms-based models. It is worth noting that utilization WaOA with battery modeling stands as a reliable tool for attaining precise model.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006315
    DOI: 10.1016/j.energy.2024.130859
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    References listed on IDEAS

    as
    1. Xu, Zhicun & Xie, Naiming & Diao, Huakang, 2023. "Lithium-ion battery state of health monitoring based on an adaptive variable fractional order multivariate grey model," Energy, Elsevier, vol. 283(C).
    2. Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    3. Wang, Qiao & Ye, Min & Cai, Xue & Sauer, Dirk Uwe & Li, Weihan, 2023. "Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications," Applied Energy, Elsevier, vol. 350(C).
    4. Zha, Yunfei & He, Shunquan & Meng, Xianfeng & Zuo, Hongyan & Zhao, Xiaohuan, 2023. "Heat dissipation performance research between drop contact and immersion contact of lithium-ion battery cooling," Energy, Elsevier, vol. 279(C).
    5. Zuo, Hongyan & Liang, Jingwei & Zhang, Bin & Wei, Kexiang & Zhu, Hong & Tan, Jiqiu, 2023. "Intelligent estimation on state of health of lithium-ion power batteries based on failure feature extraction," Energy, Elsevier, vol. 282(C).
    6. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization," Energy, Elsevier, vol. 204(C).
    7. Lai, Qingzhi & Ahn, Hyoung Jun & Kim, YoungJin & Kim, You Na & Lin, Xinfan, 2021. "New data optimization framework for parameter estimation under uncertainties with application to lithium-ion battery," Applied Energy, Elsevier, vol. 295(C).
    8. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "An interpretable state of health estimation method for lithium-ion batteries based on multi-category and multi-stage features," Energy, Elsevier, vol. 283(C).
    9. Yao, Jiachi & Han, Te, 2023. "Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data," Energy, Elsevier, vol. 271(C).
    10. Guo, Feng & Hu, Guangdi & Xiang, Shun & Zhou, Pengkai & Hong, Ru & Xiong, Neng, 2019. "A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters," Energy, Elsevier, vol. 178(C), pages 79-88.
    11. 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).
    12. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    13. Shen, Jiangwei & Ma, Wensai & Shu, Xing & Shen, Shiquan & Chen, Zheng & Liu, Yonggang, 2023. "Accurate state of health estimation for lithium-ion batteries under random charging scenarios," Energy, Elsevier, vol. 279(C).
    14. Hou, Jie & Liu, Jiawei & Chen, Fengwei & Li, Penghua & Zhang, Tao & Jiang, Jincheng & Chen, Xiaolei, 2023. "Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter," Energy, Elsevier, vol. 271(C).
    15. Perez Estevez, Manuel Antonio & Calligaro, Sandro & Bottesi, Omar & Caligiuri, Carlo & Renzi, Massimiliano, 2021. "An electro-thermal model and its electrical parameters estimation procedure in a lithium-ion battery cell," Energy, Elsevier, vol. 234(C).
    16. Mahdy, Ahmed & Hasanien, Hany M. & Turky, Rania A. & Abdel Aleem, Shady H.E., 2023. "Modeling and optimal operation of hybrid wave energy and PV system feeding supercharging stations based on golden jackal optimal control strategy," Energy, Elsevier, vol. 263(PD).
    17. Deng, Yuanwang & Ying, Hejie & E, Jiaqiang & Zhu, Hao & Wei, Kexiang & Chen, Jingwei & Zhang, Feng & Liao, Gaoliang, 2019. "Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries," Energy, Elsevier, vol. 176(C), pages 91-102.
    18. Li, Yang & Wang, Shunli & Chen, Lei & Qi, Chuangshi & Fernandez, Carlos, 2023. "Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 282(C).
    19. Khosravi, Nima & Dowlatabadi, Masrour & Abdelghany, Muhammad Bakr & Tostado-Véliz, Marcos & Jurado, Francisco, 2024. "Enhancing battery management for HEVs and EVs: A hybrid approach for parameter identification and voltage estimation in lithium-ion battery models," Applied Energy, Elsevier, vol. 356(C).
    20. Xiong, Wei & Xie, Fang & Xu, Gang & Li, Yumei & Li, Ben & Mo, Yimin & Ma, Fei & Wei, Keke, 2023. "Co-estimation of the model parameter and state of charge for retired lithium-ion batteries over a wide temperature range and battery degradation scope," Renewable Energy, Elsevier, vol. 218(C).
    21. Li, Xiaoyu & Lyu, Mohan & Li, Kuo & Gao, Xiao & Liu, Caixia & Zhang, Zhaosheng, 2023. "Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse Bayesian learning," Energy, Elsevier, vol. 282(C).
    22. Ye, Min & Guo, Hui & Xiong, Rui & Yu, Quanqing, 2018. "A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries," Energy, Elsevier, vol. 144(C), pages 789-799.
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