Application of wavelet transform-based discharging/charging voltage signal denoising for advanced data-driven SOC estimator

Abstract

Unexpected sensing of noisy discharging/charging voltage of a Li-Ion cell may result in erroneous state-of-charge (SOC) estimation and low battery management system (BMS) performance. Therefore, this study gives insight to the design and implementation of the discrete wavelet transform (DWT)-based denoising technique for noise reduction of the DCV. The steps of denoising of noisy DCV for proposed study are follows. Firstly, by using the multi-resolution analysis (MRA), the noiseriding DCV signal is decomposed into different frequency subbands. Specifically, the signal processing considering high frequency component that focuses on short-time interval is absolutely necessary in order to reduce noise of the DCV. Secondly, the hard-thresholding based denoising technique is used to adjust the wavelet coefficients of the DWT for a clear separation between the signal and the noise. Thirdly, the desired de-noised DCV signal is reconstructed by taking the inverse DWT on filtered detailed coefficients. Consequently, this signal is applied to the equivalent circuit model (ECM)-based SOC estimation algorithm using the extended Kalman filter (EKF). Experimental results indicate the robustness of the proposed work. I.INTRODUCTION Because of high specific energy density and long cycle life as well as low self-discharge, Li-Ion cell has been more increasingly recognized as a promising solution for electricpowered transportation such as electric vehicle (EV) and hybrid electric vehicle (HEV) application [1]. Specifically, with the increased interest in EV and HEV, the need for accurate and reliable knowledge in order to guarantee the overall system performance, namely a battery management system (BMS) is significantly increased together. Therefore, numerous studies have investigated to the design an improved BMS, in particular, state-of-charge (SOC) which is considered as one of the key factors in BMS for supporting optimal performance [2]. Precise SOC information is very critical in practical applications where it is necessary to determine how long the cell will last for predicting a reliable operating range,

A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network

“…Most researches mainly focus on the denoising ability of discrete wavelet transform (DWT) [40][41][42]. The hybrid WNN [43] -based SOC estimation methods; adaptive WNN-based [32] in 2005; and momentum-optimized, adaptive WNN [31]-based in 2013 SOC estimation methods are discussed, which have good performance facing the high nonlinear battery system, but the training algorithm is based on the steepest descent method, and the study of the hybrid WNN using the wavelet multi-resolution decomposition method to optimized the adaptive WNN is quite limited.…”

Online Estimation of Power Capacity With Noise Effect Attenuation for Lithium-Ion Battery

“…The noise effect attenuation is therefore critical to improve the performance of BMSs. The wavelet transform was adopted in [24,25] to de-noise the corrupted current and voltage signals so as to estimate the SOC more accurately. However, the model identification as the prerequisite of state estimate has been overlooked.…”