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State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm

Author

Listed:
  • Bingzi Cai

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Mutian Li

    (School of Automation, Central South University, Changsha 410083, China)

  • Huawei Yang

    (Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32304, USA)

  • Chunsheng Wang

    (School of Automation, Central South University, Changsha 410083, China)

  • Yougen Chen

    (School of Automation, Central South University, Changsha 410083, China)

Abstract

The accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the AdaBoost–BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy and poor generalization ability. The proposed model employs a back propagation neural network (BPNN) for the preliminary estimation. Subsequently, an AdaBoost–BPNN model is developed as a strong learner using the AdaBoost integration algorithm. Each BPNN sub-model serves as a weak learner within the AdaBoost framework. The final output of the strong learner is obtained by combining the individual outputs from the weak learners using weighting factors. This adaptive adjustment of weighting factors enhances the accuracy of SOC estimation. The proposed SOC estimation algorithm is evaluated and validated through experimental analysis. Throughout the paper, theoretical analysis is conducted, and the proposed AdaBoost–BPNN model is validated and verified using experimental results. The results demonstrate that the AdaBoost–BPNN model outperforms traditional methods in accurately estimating SOC under various conditions, including constant current-constant voltage (CCCV) charging, dynamical stress testing (DST), US06, a federal urban driving schedule (FUDS), and pulse discharge conditions.

Suggested Citation

  • Bingzi Cai & Mutian Li & Huawei Yang & Chunsheng Wang & Yougen Chen, 2023. "State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm," Energies, MDPI, vol. 16(23), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7824-:d:1289652
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    References listed on IDEAS

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    1. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(C).
    2. Jaber Abu Qahouq & Yuan Cao, 2018. "Control Scheme and Power Electronics Architecture for a Wirelessly Distributed and Enabled Battery Energy Storage System," Energies, MDPI, vol. 11(7), pages 1-20, July.
    3. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
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