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Energies 2018, 11(5), 1144; https://doi.org/10.3390/en11051144

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

1,2
,
1,2,* and 1,2
1
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China
2
Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China
*
Author to whom correspondence should be addressed.
Received: 31 December 2017 / Revised: 23 April 2018 / Accepted: 24 April 2018 / Published: 4 May 2018
(This article belongs to the Special Issue The International Symposium on Electric Vehicles (ISEV2017))
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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. View Full-Text
Keywords: discrete wavelet transform; denoising; state of charge (SOC); adaptive extended Kalman filter discrete wavelet transform; denoising; state of charge (SOC); adaptive extended Kalman filter
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Wang, X.; Xu, J.; Zhao, Y. Wavelet Based Denoising for the Estimation of the State of Charge for Lithium-Ion Batteries. Energies 2018, 11, 1144.

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