Open AccessA novel state of health estimation method for lithium-ion battery based on partial incremental capacity and Support vector regression
Author(s) -
Bo Yang,
Peng Jin,
Xiawei Liao,
Xiaomin Ma,
Wei Sun
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/804/4/042004
Subject(s) - state of health , support vector machine , battery (electricity) , computer science , state of charge , partial least squares regression , artificial intelligence , power (physics) , machine learning , physics , quantum mechanics
State of Health (SOH) is critical for lithium-ion batteries as it ensures the safety of batteries’ health condition and provides a basis for retirement of the batteries. In order to provide an accurate estimation of the SOH, a novel hybrid estimation method based on the partial incremental capacity and Support vector regression (SVR) is proposed in this paper. Firstly, the Savitzky-Golay method is applied to smooth the initial incremental capacity curves under the period of constant current charge. Then the key health features are extracted from the partial incremental curve theoretically and selected through correlation analysis methods. Finally, an SVR model is constructed to estimate the SOH. Several battery datasets under different cycling test conditions are used to validate the effectiveness of the proposed method. The result shows that the proposed method can provide a reliable and accurate estimation for SOH.