A Stable Lithium-Ion Battery SOH Estimation Framework for Suppressing Measurement Noise With Unknown Distribution

Abstract

Existing methods for estimating the state of health (SOH) of lithium-ion battery (LIB) rely typically on the assumption that the distribution of noise (or outliers) in the measurement data is known. This assumption, however, rarely holds true for LIB operating under real-world conditions. This article proposes a stable framework for accurate SOH estimation that accommodates noises with unknown distribution in both measurement data and label values. The framework combines generalized correntropy loss (GCL) with Savitzky-Golay (SG) filter and extreme learning machine (ELM) to obtain measurement data filter named SG filter with GCL (SG-GCL) and SOH estimator named generalized ELM (GELM), respectively. The SG-GCL filtering of the measurement data keeps the root-mean-square error (RMSE) within 0.0365%, and Pearson correlation between extracted feature and SOH improves by 0.4963, which, in turn, leads to the reduction of the RMSE metrics of the ELM for the estimation of the SOH by 43.69%. From the filtering results, feature extraction and estimation results proved its necessity and effectiveness. GELM effectively suppresses the influence of label value noise on the model in the training process, which reduces the SOH estimation RMSE index by more than 0.66%. The results from experiments with different distributional noise conditions show that the proposed SOH estimation framework has excellent and stable performance.