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

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 noise-riding 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.