Lithium Battery State-of-Health Estimation Based on Sample Data Generation and Temporal Convolutional Neural Network

Fang Guo, Guangshan Huang, Wencan Zhang (), An Wen, Taotao Li, Hancheng He, Haolin Huang and Shanshan Zhu
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Fang Guo: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China
Guangshan Huang: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China
Wencan Zhang: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China
An Wen: Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Huzhou 313098, China
Taotao Li: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China
Hancheng He: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China
Haolin Huang: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China
Shanshan Zhu: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China

Energies, 2023, vol. 16, issue 24, 1-15

Abstract: Accurate estimation of battery health is an effective means of improving the safety and reliability of electrical equipment. However, developing data-driven models to estimate battery state of health (SOH) is challenging when the amount of data is restricted. In this regard, this study proposes a method for estimating the SOH of lithium batteries based on sample data generation and a temporal convolutional neural network. First, we analyzed the charge/discharge curves of the batteries, from which we extracted features that were highly correlated with the SOH decay. Then, we used a Variational Auto-Encoder (VAE) to learn the features and distributions of the sample data to generate highly similar data and enrich the number of samples. Finally, a temporal convolutional neural network (TCN) was built to mine the nonlinear relationship between features and SOH by combining the source and extended domain data to realize SOH estimation. The experimental results show that the proposed method in this study has less than 2% error in SOH estimation, which improves the accuracy by 64.9% based on its baseline model. The feasibility of using data-driven models for battery health management in data-constrained application scenarios is demonstrated.

Keywords: battery; state of health; limited data; sample generation; Variational Auto-Encoder; temporal convolutional neural network (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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