Thermoelastic guided wave behavior modeling and distributed experimental analysis of the inhomogeneous state of charge for silicon carbon lithium-ion batteries

Abstract

Complex working conditions and state of charge (SOC) uneven distribution are both key factors inducing the performance degradation of lithium-ion batteries. To address this issue, a novel distributed thermoelastic guided wave testing technique is proposed to realize the SOC distribution testing of multi-regional silicon carbon lithium-ion batteries. Since the ultrasonic testing method can acquire the changes of material properties, the features among the time domain signals and SOC under complex working conditions (different C rate and temperature) are extracted experimentally, which reveals the strong correlation between the acoustic characterization parameters and the amount of cell expansion. Moreover, the theoretical model of wave propagation in multi-layered porous silicon carbon lithium-ion battery versus temperature is built to depict the wave mechanism. It is constructed by state vector and Legendre polynomial hybrid method in the context of Biot theory and Green-Naghdi theory. By comparing the experimental and theoretical group velocities in different temperatures and SOC, the two are in good agreement. It verifies the validity of the theoretical model and experimental method. Subsequently, the proposed thermoelastic guided wave testing method is used to determine the difference of the time domain parameters in the different receiving regions and SOC evolution stages. The results indicate that the acoustic response changes significantly in different regions and stages, and the SOC uneven distribution is particularly dramatic in the low SOC stage, and the response becomes more sensitive the further away from the center position of the propagation region. The connection between the thermoelastic guided wave behavior and the SOC make it possible for non-destructive monitoring of the silicon carbon lithium-ion battery SOC. Our proposed method is beneficial for evaluating the potential performance degradation due to SOC uneven distribution.