Measurement of Particle Size Distribution of Battery Slurries Using Ultrasonic Attenuation Spectroscopy

doi:10.7503/cjcu20220040

Abstract

Abstract:

The particle size distribution and dispersion uniformity of the granular active material in the battery slurries have a direct impact on the important properties of the battery such as internal resistance， voltage， local surface current， and total polarization degree. It is of great significance for the quality control of the battery if its present line can be measured in real time. Based on the characteristics of high solid content， high viscosity and low transmittance of battery slurry， this paper investigates the measurement of its particle size distribution by ultrasonic attenuation spectroscopy. The biggest difficulty in applying to the particle size distribution measurement of battery slurries is that its model for predicting the particle size distribution using the ultrasound attenuation spectroscopy requires difficult-to-obtain physical parameters of the dispersed and continuous phases. In this paper， principal component analysis（PCA） combined with error back propagation（BP） neural network is proposed to establish a prediction model to solve the difficulties of ultrasonic attenuation spectroscopy method， and genetic algorithm（GA） is introduced to optimize the initial weights and thresholds of the BP network. Combined with the battery slurry with LiCoO₂ as the active material for validation， the results show that the PCA-GA-BP neural network can effectively predict the particle size distribution of battery slurry with different solid contents， and the predicted values have high peak shape overlap with the real values and small peak height deviation， with the mean square error of 0.1358 and the degree of fit（R²） of 0.9816， indicating that ultrasonic attenuation spectroscopy can be used as an important way to measure the particle size distribution of battery slurry.

Key words: Ultrasonic attenuation spectroscopy, Particle size distribution, Battery slurry, BP neural network, Principal component analysis, Genetic algorithm

CLC Number:

O657

TrendMD:

HUANG Mingxin, ZHOU Lei, WANG Xuezhong. Measurement of Particle Size Distribution of Battery Slurries Using Ultrasonic Attenuation Spectroscopy[J]. Chem. J. Chinese Universities, 2022, 43(6): 20220040.

Figures/Tables 7

References 30

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