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Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR

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  • Chunling Wu

    (School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China)

  • Juncheng Fu

    (School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China)

  • Xinrong Huang

    (School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China)

  • Xianfeng Xu

    (School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China)

  • Jinhao Meng

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Accurate estimation of the state-of-health (SOH) of lithium-ion batteries is a crucial reference for energy management of battery packs for electric vehicles. It is of great significance in ensuring safe and reliable battery operation while reducing maintenance costs of the battery system. To eliminate the nonlinear effects caused by factors such as capacity regeneration on the SOH sequence of batteries and improve the prediction accuracy and stability of lithium-ion battery SOH, a prediction model based on Variational Modal Decomposition (VMD) and Dung Beetle Optimization -Support Vector Regression (DBO-SVR) is proposed. Firstly, the VMD algorithm is used to decompose the SOH sequence of lithium-ion batteries into a series of stationary mode components. Then, each mode component is treated as a separate subsequence and modeled and predicted directly using SVR. To address the problem of difficult parameter selection for SVR, the DBO algorithm is used to optimize the parameters of the SVR model before training. Finally, the predicted values of each subsequence are added and reconstructed to obtain the final SOH prediction. In order to verify the effectiveness of the proposed method, the VMD-DBO-SVR model was compared with SVR, Empirical Mode Decomposition-Support Vector Regression (EMD-SVR), and VMD-SVR methods for SOH prediction of batteries based on the NASA dataset. Experimental results show that the proposed model has higher prediction accuracy and fitting degree, with prediction errors all within 1% and better robustness.

Suggested Citation

  • Chunling Wu & Juncheng Fu & Xinrong Huang & Xianfeng Xu & Jinhao Meng, 2023. "Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR," Energies, MDPI, vol. 16(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:3993-:d:1142857
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    References listed on IDEAS

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    1. Chen, Dan & Meng, Jinhao & Huang, Huanyang & Wu, Ji & Liu, Ping & Lu, Jiwu & Liu, Tianqi, 2022. "An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity diving," Energy, Elsevier, vol. 245(C).
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    3. Phattara Khumprom & Nita Yodo, 2019. "A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm," Energies, MDPI, vol. 12(4), pages 1-21, February.
    4. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    5. Shuo Sun & Junzhong Sun & Zongliang Wang & Zhiyong Zhou & Wei Cai, 2022. "Prediction of Battery SOH by CNN-BiLSTM Network Fused with Attention Mechanism," Energies, MDPI, vol. 15(12), pages 1-17, June.
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    Cited by:

    1. Fang Guo & Guangshan Huang & Wencan Zhang & An Wen & Taotao Li & Hancheng He & Haolin Huang & Shanshan Zhu, 2023. "Lithium Battery State-of-Health Estimation Based on Sample Data Generation and Temporal Convolutional Neural Network," Energies, MDPI, vol. 16(24), pages 1-15, December.

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