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Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

Author

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  • Yan Cheng

    (Department of Automotive Engineering, Hebei Jiaotong Vocational and Technical College, Shijiazhuang 266100, China)

  • Xuesen Zhang

    (School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 266100, China)

  • Xiaoqiang Wang

    (School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 266100, China)

  • Jianhua Li

    (School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 266100, China)

Abstract

The traditional battery state of charge (SOC) estimation method, which is based on neural networks, directly uses terminal voltage and terminal current as the input data. Although it is convenient to implement, it produces a large estimation error when the current and voltage change drastically. To solve this problem, a new method, which uses a composite multiscale wavelet transform, is proposed to estimate the battery SOC. In the proposed method, a wavelet transform is applied to the input data, and this process obtains the approximate coefficients and detail coefficients of the input data at different scales. A neural network then uses these coefficients as inputs to estimate the SOC. The experimental results show that the proposed method can improve the accuracy of the battery SOC estimation without changing the neural network structure or algorithm.

Suggested Citation

  • Yan Cheng & Xuesen Zhang & Xiaoqiang Wang & Jianhua Li, 2022. "Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform," Energies, MDPI, vol. 15(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2064-:d:769277
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    References listed on IDEAS

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    Cited by:

    1. Suwei Zhai & Wenyun Li & Cheng Wang & Yundi Chu, 2022. "A Novel Data-Driven Estimation Method for State-of-Charge Estimation of Li-Ion Batteries," Energies, MDPI, vol. 15(9), pages 1-17, April.
    2. Jikai Bi & Jae-Cheon Lee & Hao Liu, 2022. "Performance Comparison of Long Short-Term Memory and a Temporal Convolutional Network for State of Health Estimation of a Lithium-Ion Battery using Its Charging Characteristics," Energies, MDPI, vol. 15(7), pages 1-24, March.

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