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Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm

Author

Listed:
  • Shuo Sun

    (Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China)

  • Qianli Zhang

    (College of Engineering, Ocean University of China, Qingdao 266042, China)

  • Junzhong Sun

    (Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China)

  • Wei Cai

    (Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China)

  • Zhiyong Zhou

    (Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China)

  • Zhanlu Yang

    (Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China)

  • Zongliang Wang

    (Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, China)

Abstract

Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the AdaBoost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved AdaBoost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model.

Suggested Citation

  • Shuo Sun & Qianli Zhang & Junzhong Sun & Wei Cai & Zhiyong Zhou & Zhanlu Yang & Zongliang Wang, 2022. "Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm," Energies, MDPI, vol. 15(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5842-:d:886008
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    References listed on IDEAS

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    1. Pan, Haihong & Lü, Zhiqiang & Lin, Weilong & Li, Junzi & Chen, Lin, 2017. "State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model," Energy, Elsevier, vol. 138(C), pages 764-775.
    2. Chaoran Li & Fei Xiao & Yaxiang Fan, 2019. "An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit," Energies, MDPI, vol. 12(9), pages 1-22, April.
    3. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
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    Cited by:

    1. Arindita Saha & Puja Dash & Naladi Ram Babu & Tirumalasetty Chiranjeevi & Bathina Venkateswararao & Łukasz Knypiński, 2022. "Impact of Spotted Hyena Optimized Cascade Controller in Load Frequency Control of Wave-Solar-Double Compensated Capacitive Energy Storage Based Interconnected Power System," Energies, MDPI, vol. 15(19), pages 1-25, September.
    2. Olivér Hornyák & László Barna Iantovics, 2023. "AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics," Mathematics, MDPI, vol. 11(8), pages 1-24, April.

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