IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i23p5920-d1529325.html
   My bibliography  Save this article

State of Charge Prediction of Mine-Used LiFePO 4 Battery Based on PSO-Catboost

Author

Listed:
  • Dazhong Wang

    (CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030006, China
    Shanxi Tiandi Coal Mining Machinery Co., Ltd., Taiyuan 030006, China
    China National Engineering Laboratory for Coal Mining Machinery, Taiyuan 030006, China)

  • Yinghui Chang

    (CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030006, China
    Shanxi Tiandi Coal Mining Machinery Co., Ltd., Taiyuan 030006, China
    China National Engineering Laboratory for Coal Mining Machinery, Taiyuan 030006, China)

  • Pengfei Ji

    (CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030006, China
    Shanxi Tiandi Coal Mining Machinery Co., Ltd., Taiyuan 030006, China
    China National Engineering Laboratory for Coal Mining Machinery, Taiyuan 030006, China
    Yulin Tiandi Coal Mining Machinery Co., Ltd., Yulin 719000, China)

  • Yanchun Suo

    (CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030006, China
    Shanxi Tiandi Coal Mining Machinery Co., Ltd., Taiyuan 030006, China
    China National Engineering Laboratory for Coal Mining Machinery, Taiyuan 030006, China)

  • Ning Chen

    (CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030006, China
    Shanxi Tiandi Coal Mining Machinery Co., Ltd., Taiyuan 030006, China
    China National Engineering Laboratory for Coal Mining Machinery, Taiyuan 030006, China)

Abstract

The accurate prediction of battery state of charge (SOC) is one of the critical technologies for the safe operation of a power battery. Aiming at the problem of mine power battery SOC prediction, based on the comparative experiments and analysis of particle swarm optimization (PSO) and Categorical Boosting (Catboost) characteristics, the PSO-Catboost model is proposed to predict the SOC of a power lithium iron phosphate battery. Firstly, the classification model based on Catboost is constructed, and then the particle swarm algorithm is used to optimize the Catboost hyperparameters to build the optimal model. The experiment and comparison show that the optimized model’s prediction accuracy and average precision are superior to other comparative models. Compared with the Catboost model, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of the PSO-Catboost model decreased by 12.4% and 25.4% during charging and decreased by 5.5% and 12.2% during discharging. Finally, the Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models, both ensemble learning models, are selected and compared with PSO-Catboost after being optimized via PSO. The experimental results show that the proposed model has a better performance. In this paper, experiments show that the optimization model can select parameters more intelligently, reduce the error caused by artificial experience to adjust parameters, and have a better theoretical value and practical significance.

Suggested Citation

  • Dazhong Wang & Yinghui Chang & Pengfei Ji & Yanchun Suo & Ning Chen, 2024. "State of Charge Prediction of Mine-Used LiFePO 4 Battery Based on PSO-Catboost," Energies, MDPI, vol. 17(23), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5920-:d:1529325
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/23/5920/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/23/5920/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Song, Yuchen & Liu, Datong & Liao, Haitao & Peng, Yu, 2020. "A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 261(C).
    2. Deng, Zhongwei & Hu, Xiaosong & Lin, Xianke & Che, Yunhong & Xu, Le & Guo, Wenchao, 2020. "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, Elsevier, vol. 205(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei, Jingwen & Chen, Chunlin, 2021. "A multi-timescale framework for state monitoring and lifetime prognosis of lithium-ion batteries," Energy, Elsevier, vol. 229(C).
    2. Panagiotis Eleftheriadis & Spyridon Giazitzis & Sonia Leva & Emanuele Ogliari, 2023. "Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview," Forecasting, MDPI, vol. 5(3), pages 1-24, September.
    3. Zhou, Yifei & Wang, Shunli & Feng, Renjun & Xie, Yanxin & Fernandez, Carlos, 2024. "Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation," Energy, Elsevier, vol. 312(C).
    4. Chen, Shouxuan & Zhang, Shuting & Geng, Yuanfei & Jia, Yao & Zhang, Shuzhi, 2025. "Dynamic conditions-oriented model-data fused framework enabling state of charge and capacity accurate co-estimation of lithium-ion battery," Energy, Elsevier, vol. 317(C).
    5. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    6. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
    7. Fan, Xinyuan & Zhang, Weige & Zhang, Caiping & Chen, Anci & An, Fulai, 2022. "SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture," Energy, Elsevier, vol. 256(C).
    8. Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
    9. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    10. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    11. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
    12. Zhang, Shuzhi & Zhang, Chen & Jiang, Shiyong & Zhang, Xiongwen, 2022. "A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation," Energy, Elsevier, vol. 246(C).
    13. Jiwei Wang & Hao Li & Chunling Wu & Yujun Shi & Linxuan Zhang & Yi An, 2024. "State of Health Estimations for Lithium-Ion Batteries Based on MSCNN," Energies, MDPI, vol. 17(17), pages 1-21, August.
    14. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    15. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    16. Yi, Yahui & Xia, Chengyu & Shi, Lei & Meng, Leifeng & Chi, Qifu & Qian, Liqin & Ma, Tiancai & Chen, Siqi, 2024. "Lithium-ion battery expansion mechanism and Gaussian process regression based state of charge estimation with expansion characteristics," Energy, Elsevier, vol. 292(C).
    17. Yong Li & Jue Yang & Wei Long Liu & Cheng Lin Liao, 2020. "Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery," Energies, MDPI, vol. 13(15), pages 1-23, July.
    18. Chen, Zhen & Liu, Weijie & Zhou, Di & Xia, Tangbin & Pan, Ershun, 2025. "Inconsistency identification for Lithium-ion battery energy storage systems using deep embedded clustering," Applied Energy, Elsevier, vol. 388(C).
    19. Qian, Cheng & Guan, Hongsheng & Xu, Binghui & Xia, Quan & Sun, Bo & Ren, Yi & Wang, Zili, 2024. "A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions," Energy, Elsevier, vol. 294(C).
    20. Li, Qingbo & Lu, Taolin & Lai, Chunyan & Li, Jiwei & Pan, Long & Ma, Changjun & Zhu, Yunpeng & Xie, Jingying, 2024. "Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation," Energy, Elsevier, vol. 290(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5920-:d:1529325. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.