Health Factor Extraction of Lithium-Ion Batteries Based on Discrete Wavelet Transform and SOH Prediction Based on CatBoost

Mei Zhang, Wanli Chen, Jun Yin and Tao Feng
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Mei Zhang: College of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, China
Wanli Chen: College of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, China
Jun Yin: College of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, China
Tao Feng: College of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, China

Energies, 2022, vol. 15, issue 15, 1-17

Abstract: Aiming to accurately identify the state of health (SOH) and the remaining useful life (RUL) of lithium-ion batteries, in this paper, we propose an algorithm for the health factor extraction and SOH prediction of the batteries based on discrete wavelet transform and the Cauchy–Gaussian variation tent sparrow search algorithm (DWT-CGTSSA). Firstly, concerning the inconsistent data length, discrete wavelet transform (DWT) was adopted to decompose the battery’s signals and extract features. Then, the Cauchy–Gaussian variation tent sparrow search algorithm (CGTSSA) was utilized to extract features and obtain the optimal feature subset after encoding. Finally, the optimal feature subset was used to establish a prediction model based on CatBoost for predicting the SOH of lithium-ion batteries. Experiments were conducted for verification. The experimental results showed that the model established in this research is capable of realizing the prediction between different battery packs. The B0005 battery from dataset A was taken as the training set to predict the complete SOH of B0006 and B0007 batteries. For the prediction model of CGTSSA-CatBoost, the goodness of fit (R 2 ) exceeded 0.99, and the value of mean square error (MSE) was less than 1‰. A comparison with other state-of-the-art prediction models verified the superior performance of the CGTSSA-CatBoost model. Under different working conditions, the R 2 of all models in dataset B exceeded 0.98.

Keywords: lithium-ion battery; SOH prediction; DWT; CGTSSA; CatBoost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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