Investigation of noise suppression in experimental multi-cell battery string voltage applying various mother wavelets and decomposition levels in discrete wavelet transform for precise state-of-charge estimation

Abstract

Lithium-ion battery state-of-charge (SOC) estimation algorithms developed employing electrical circuit models (ECMs) require original experimental discharging and charging voltage (EDCV) data for very high accuracy. However, if noisy EDCV signals are detected while sensing, inaccurate SOC estimation, which causes poor BMS performance, is unavoidable. Thus, this paper introduces a novel method for implementing noise suppression employing a discrete wavelet transform (DWT) multi-resolution analysis (MRA) based method. The approach extensively performs two comparison evaluations of denoising based on the information provided by the mother wavelet and the corresponding MRA level decomposition. The former approach is to obtain the level of decomposition with high execution of the denoising under a similar mother wavelet state. The second approach, on the other hand, compares noise suppression for various mother wavelets under identical conditions of the level decomposition with similar decomposition level conditions. Signal-to-noise ratio (SNR) is used to evaluate all comparative analyses. This method compares both hard- and soft-thresholding noise reduction methods in their entirety. From these findings, the study's analytical findings indicate a precise evaluation by demonstrating the variation in SNR with the corresponding mother wavelets and their decomposition levels. Thus, it is certain that the mother wavelet and the decomposition level are optimized for noise suppression for a battery string, series/parallel (2S3P) each of 2.2 Ah, with the EDCV signal incorporated with noise.