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Wavelet Based Denoising for the Estimation of the State of Charge for Lithium-Ion Batteries

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  • Xiao Wang

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China
    Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China)

  • Jun Xu

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China
    Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China)

  • Yunfei Zhao

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China
    Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China)

Abstract

In practical electric vehicle applications, the noise of original discharging/charging voltage (DCV) signals are inevitable, which comes from electromagnetic interference and the measurement noise of the sensors. To solve such problems, the Discrete Wavelet Transform (DWT) based state of charge (SOC) estimation method is proposed in this paper. Through a multi-resolution analysis, the original DCV signals with noise are decomposed into different frequency sub-bands. The desired de-noised DCV signals are then reconstructed by utilizing the inverse discrete wavelet transform, based on the sure rule. With the de-noised DCV signal, the SOC and the parameters are obtained using the adaptive extended Kalman Filter algorithm, and the adaptive forgetting factor recursive least square method. Simulation and experimental results show that the SOC estimation error is less than 1%, which indicates an effective improvement in SOC estimation accuracy.

Suggested Citation

  • Xiao Wang & Jun Xu & Yunfei Zhao, 2018. "Wavelet Based Denoising for the Estimation of the State of Charge for Lithium-Ion Batteries," Energies, MDPI, vol. 11(5), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1144-:d:144536
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    References listed on IDEAS

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