Self‐Discharge Voltage Drop Estimation Method Based on Improved Gaussian Process Regression

• Published in: Energy technology (Weinheim, Germany) Germany), 2022-07, Vol.10 (7), p.n/a
• Main Authors: Liu, Zhengyu, Huang, Wei, Wang, Keqing, Xie, Juan, He, Huijuan
• Format: Article
• Language: English
• Subjects: Algorithms; Angle of reflection; Correlation analysis; Discharge; Feature extraction; Gaussian process; Gaussian process regression; kernel function; Kernel functions; Lithium; Lithium-ion batteries; Particle swarm optimization; particle swarm optimization algorithm; Regression models; self-discharge; Voltage drop
• ISSN: 2194-4288; 2194-4296
• DOI: 10.1002/ente.202200402

The self‐discharge rate is an important index for determining the quality of a lithium‐ion battery. Currently, the self‐discharge of a battery is mainly determined through experimental tests, which are time‐consuming and laborious. To provide a fast and reliable self‐discharge rate estimation, an improved Gaussian process regression (GPR) model based on the charge–discharge curve is proposed to estimate the self‐discharge voltage drop. In this method, the voltage features extracted from the charge–discharge curve are used as the input of the GPR model. These features reflect the self‐discharge performance of batteries from different angles. The gray correlation analysis method is used to analyze the degree of correlation between the selected features and self‐discharge voltage drop. Furthermore, a kernel function suitable for voltage drop estimation is selected. Simultaneously, the GPR model is adjusted, and the particle swarm optimization algorithm is used to optimize the superparameters of the GPR model to improve the estimation accuracy of the self‐discharge voltage drop of the model. The self‐discharge voltage drop data from the experiment are used to demonstrate the estimation effect of the proposed method. The results reveal that this method is reliable and has a high estimation accuracy for the self‐discharge voltage drop. Herein, the features are extracted from the charge–discharge curve of a lithium‐ion battery, and the self‐discharge voltage drop is estimated by using the improved Gaussian process regression model. Compared with the experimental measurement method, the test efficiency of the self‐discharge voltage drop is improved.