Noise-resistant state of charge estimation of Li-ion battery using the outlier robust extreme learning machine

Abstract

The State of Charge (SOC) indicates the amount of charge remaining in the battery, which is a critical parameter in the battery management system (BMS). A precise SOC estimation can effectively protect the battery and extend its service life. Recently, a leading-edge machine learning model, namely the extreme learning machine (ELM), has been applied for SOC estimation due to its powerful fitting and fast training capabilities. However, as BMS is required to operate in more and more complex environments, it is inevitable to encounter the effects of non-Gaussian noises caused by system errors, human causes, and other factors. These noises are random, sparse, and far from the targets, mainly referring to the outliers. The classical extreme learning machine (ELM) is susceptible to noise, leading to poor performance in outlier-contaminated datasets. To overcome this problem, we developed a new robust SOC estimation method through the outlier robust ELM (OR-ELM) in this paper. Additionally, a powerful iterative algorithm, namely the alternating direction method of multipliers (ADMM), was utilized for training OR-ELM. Experiments were carried out on a dataset of Panasonic 18650 cells, and the results show the applicability and robustness of OR-ELM in the SOC estimation.