Abstract
With the expansion of the battery market and electric vehicles, improved battery-state estimation approaches are required for energy management to meet the requirements for safe usage. The accurate identification of battery parameters significantly influences the state-of-charge (SOC) computation algorithms. In addition, noise from charging and discharging voltages can influence the accuracy of the SOC estimation. This paper proposes a novel methodology combining a discrete wavelet transform (DWT) denoising procedure and an adaptive parameter identification. The DWT uses a multi-resolution analysis (MRA) to decompose noise-riding charging/discharge voltage signals, which are then rebuilt using the inverse DWT (IDWT) based on the rigsure adaptive threshold selection rule. In this study, a dual-resistor-capacitor lithium-ion battery model was examined. A formulation of the modified forgetting factor recursive least squares (MFFRLS) was presented and demonstrated robustness in overcoming data saturation while enhancing parameter tracking ability even under the effect of noise. To verify the effectiveness of the coupled methods for SOC estimation defined in this study, experimental data obtained from various working conditions were employed to estimate the SOC using the extended Kalman filter (EKF). A thorough analysis demonstrated that the proposed MFFRLS combined with the denoising method performed well in terms of SOC estimation accuracy and robustness.
Published Version
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