Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost

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 16, 1-18

Abstract: To mine the battery’s health factors more comprehensively and accurately identify the lithium battery’s State of Health (SOH), an Improved Douglas–Peucker feature extraction algorithm is proposed, and the LAOS-XGboost model is proposed to be used to predict the SOH of the battery. Firstly, to solve the problem that the traditional Douglas–Peucker algorithm has difficulties extracting curve features in a fixed dimension, the Douglas–Peucker algorithm is improved by de-thresholding. Then, the Wrapper method combined with the Improved Douglas–Peucker algorithm is used to construct the feature engineering of battery life prediction, and the optimal feature subset is obtained. Then, LAOS-XGboost is used to establish a battery SOH prediction model; finally, this model is used to predict the SOH of different batteries and the same battery, and the robustness of the model is analyzed. The experimental results show that the R2 of all XGboost models is higher than 0.97 in the prediction experiments of different batteries. The AE of the LAOS-XGboost model is 0, and the TIC index is less than 3% under 10 dB SNR. In the same battery prediction experiment, the TIC index of the model is less than 0.3%.

Keywords: lithium-ion battery; SOH prediction; XGboost; Douglas–Peucker algorithm; LAOS (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 (3)

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