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State of health prediction for lithium‐ion batteries with a novel online sequential extreme learning machine method
Author(s) -
Tian Huixin,
Qin Pengliang
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.5934
Subject(s) - computer science , machine learning , artificial intelligence , extreme learning machine , state of health , battery (electricity) , random forest , ensemble learning , algorithm , artificial neural network , power (physics) , physics , quantum mechanics
Summary 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.