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Rapid measurement method for lithium‐ion battery state of health estimation based on least squares support vector regression
Author(s) -
Xiao Bin,
Xiao Bing,
Liu Luoshi
Publication year - 2020
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.6194
Subject(s) - hyperparameter optimization , support vector machine , battery (electricity) , state of health , computer science , cross validation , engineering , power (physics) , artificial intelligence , physics , quantum mechanics
Summary The state of health (SoH) is a key indicator of a battery management system (BMS). Accurate SoH estimation can be adopted to guide the timely recovery and ladder utilization for lithium‐ion batteries (LiBs), which is particularly beneficial to environmental protection. Although many battery SoH estimation algorithms have been developed, there are few simple and easy‐to‐use methods for on‐site rapidly measurement. Therefore, in this paper, a model for battery SoH estimate is realized by least‐square support vector regression (LS‐SVR) configured with radial basis function (RBF) kernel. Based on the hysteresis behavior of LiB, data samples can be quickly obtained by the hybrid pulse power characteristic (HPPC) test. The grey correlation analysis (GRA) was conducted to select features of data samples, and the K‐fold cross‐validation and grid search (GS) were performed to optimize the hyperparameters of the estimation model LS‐SVR. Finally, to verify the proposed method, data samples collected from 18 650 LiB with different aging degrees were used for LS‐SVR model training and testing, and the method was compared to existing SoH estimation methods. Experimental results demonstrate that the SoH estimation model only requires some short‐term data of a battery to achieve high‐precision SoH estimation, which shows that this method has broad application prospects.