State of health prediction for lithium‐ion batteries with a novel online sequential extreme learning machine method

Abstract

State of health (SOH) prediction is always a research hotspot in the field of lithium-ion batteries (LIBs). Machine learning (ML) methods have received widespread attention for their high prediction accuracy. However, the existing studies only focus on extracting features from simple constant current charge-discharge curves or using features that require pre-processing, while the actual discharge current is random and can affect battery aging. Besides, the online sequential extreme learning machine (OSELM) currently used in ML lacks a more efficient online learning and update mechanism in terms of prediction. Therefore, this paper firstly extracts effective features from the random discharge data and conducts a mechanism analysis to verify its rationality. Then, we propose a drift detection based on the Bernstein inequality (BI-DD) algorithm and use it to guide the OSELM to save learning time. The experimental results show the OSELM based on the BI-DD can perform good learning for SOH prediction in a shorter time. The learning time can be reduced by up to 88.87% and the mean absolute error (MAE) does not exceed 1%, which is a promising SOH prediction method. Highlights Extract aging features from random discharge data and the rationality of the extracted features is analyzed according to the mechanism. A drift detection algorithm based on Bernstein inequality (BI-DD) is proposed. An OSELM based on concept drift detection is proposed and for SOH online learning and prediction.