Lithium-ion batteries are widely used in electric vehicles and energy storage systems. Sudden fire accident is one of the most serious issue, which is mainly caused by unpredicted internal short circuit. Metal particle defect is a key factor in internal short circuit it will not show an obvious abnormal change in battery external characteristic just like mechanical and thermal abuse. So, a non-destructive testing of battery internal metal defect is very necessary. This study is first time to scan and analyze different types of defects inside a battery by using ultrasonic technology, and it shows the detection capability boundary of this methodology. A non-contact ultrasonic scanning system with multi-channel was built to scan the battery sample with aluminum foil, copper foil and copper powder defects. The position and shape of those defects were clearly shown by using tomography methodology. It was found that the acoustic properties difference between metal defects and battery active materials has a strong influence on detection sensitivity. Compared with aluminum foil, copper foil and copper powder are easier to be detected and change the ultrasonic signal greatly, they will produce an obvious shadowing artifacts and speed displacement phenomena in tomography images. Ultrasonic tomography technology is an effective method for non-destructive testing of lithium-ion batteries.

Electrochemical impedance spectroscopy (EIS) is a powerful tool for investigating electrochemical systems, such as lithium-ion batteries or fuel cells, given its high frequency resolution. The distribution of relaxation times (DRT) method offers a model-free approach for a deeper understanding of EIS data. However, in lithium-ion batteries, the differential capacity caused by diffusion processes is non-negligible and cannot be decomposed by the DRT method, which limits the applicability of the DRT method to lithium-ion batteries. In this study, a joint estimation method with Tikhonov regularization is proposed to estimate the differential capacity and the DRT simultaneously. Moreover, the equivalence of the differential capacity and the incremental capacity is proven. Different types of commercial lithium-ion batteries are tested to validate the joint estimation method and to verify the equivalence. The differential capacity is shown to be a promising approach to the evaluation of the state-of-health (SOH) of lithium-ion batteries based on its equivalence with the incremental capacity.

Lithium plating leads to severe capacity fading and possible safety problems in lithium-ion batteries (LiB). Therefore, it is necessary to provide an in-situ detection methodology of lithium plating evolution during battery cycling. Qualitative analysis of the Distribution of Relaxation Times (DRT), based on Electrochemical Impedance Spectroscopy (EIS) deconvolution, shows a different degradation mechanism between lithium plating and normal aging batteries. A Support Vector Machine (SVM) detection algorithm based on the DRT is proposed. It only relies on the measured capacity and EIS data during battery aging, which is also easy to process and non-destructive to battery sample. The method has a significant advantage in battery safety management and echelon selection of retired batteries.