State of Health Estimation and Remaining Useful Life Prediction for a Lithium-Ion Battery with a Two-Layer Stacking Regressor

Jun Yuan, Zhili Qin, Haikun Huang, Xingdong Gan, Shuguang Li and Baihai Li ()
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Jun Yuan: School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China
Zhili Qin: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Haikun Huang: School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China
Xingdong Gan: School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China
Shuguang Li: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Baihai Li: School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China

Energies, 2023, vol. 16, issue 5, 1-15

Abstract: The development of a machine-learning method with high accuracy, high generalization, and strong robustness for evaluating battery health states is essential in the field of battery health management. In this work, the data-driven stacking regressor (SR) method with a two-layer diagnostic framework was proposed to estimate the state of health (SOH) and predict the remaining useful life (RUL). Five individual estimators were merged in the first layer, including bagging, gradient boosting regression (GBR), support vector regression (SVR), Hist-GBR, and AdaBoost, and linear regression (LR) was used in the second layer to construct the SR model. The SR model produces highly accurate results without the requirement of excessive parameter adjustment. Fifteen batteries from the NASA dataset were used for our experiments, resulting in rather low values of average root mean square error (ARMSE) and relative error (RE) for the SOH estimation and RUL predictions of the different batteries, demonstrating the superiority of the SR model.

Keywords: lithium-ion battery; data-driven machine learning; stacking regressor; SOH estimation; RUL prediction (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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