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Whale Optimization Algorithm BP Neural Network with Chaotic Mapping Improving for SOC Estimation of LMFP Battery

Author

Listed:
  • Jian Ouyang

    (Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, China)

  • Hao Lin

    (School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China)

  • Ye Hong

    (Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, China)

Abstract

The state of charge (SOC) is a core parameter in the battery management system for LMFP batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. In order to achieve better SOC estimation accuracy, this article proposes an adaptive whale optimization algorithm (WOA) with chaotic mapping to improve the BP neural network (BPNN) model. The SOC estimation accuracy of the BPNN model was improved by utilizing WOA to find the optimal target weight values and thresholds. Comparative simulation experiments (including constant current and working condition discharge experiments) were conducted in Matlab/Simulink R2018a to verify the proposed algorithm and the other four algorithms. The experimental results show that the proposed algorithm had higher SOC estimation accuracy than the other four algorithms, and its prediction errors were less than 1%. This indicates that the proposed SOC estimation method has better prediction accuracy and stability, and has certain theoretical research significance.

Suggested Citation

  • Jian Ouyang & Hao Lin & Ye Hong, 2024. "Whale Optimization Algorithm BP Neural Network with Chaotic Mapping Improving for SOC Estimation of LMFP Battery," Energies, MDPI, vol. 17(17), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4300-:d:1465798
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    References listed on IDEAS

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    1. Sulaiman, Mohd Herwan & Mustaffa, Zuriani & Zakaria, Nor Farizan & Saari, Mohd Mawardi, 2023. "Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle," Energy, Elsevier, vol. 279(C).
    2. Yang, Fangfang & Xing, Yinjiao & Wang, Dong & Tsui, Kwok-Leung, 2016. "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Elsevier, vol. 164(C), pages 387-399.
    3. Zafar, Muhammad Hamza & Mansoor, Majad & Abou Houran, Mohamad & Khan, Noman Mujeeb & Khan, Kamran & Raza Moosavi, Syed Kumayl & Sanfilippo, Filippo, 2023. "Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles," Energy, Elsevier, vol. 282(C).
    4. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    5. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    6. Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
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    Cited by:

    1. Genbao Wang & Yejian Xue & Yafei Qiao & Chunyang Song & Qing Ming & Shuang Tian & Yonggao Xia, 2024. "Research on SOC Prediction of Lithium-Ion Batteries Based on OLHS-DBO-BP Neural Network," Energies, MDPI, vol. 17(23), pages 1-16, December.

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