Non-destructive visualization of short circuits in lithium-ion batteries by a magnetic field imaging system

Abstract

To develop a high-density and long-life lithium-ion battery, a technology is needed that allows non-destructive visualization of the spatial distribution of deteriorated parts after cycle test. In the present study, we measured the distribution of the magnetic field leaking from the lithium-ion battery during its operation. Based on the measurement results, we evaluated the spatial distribution of electric current density that corresponds to the reaction rate of the active material and the ion diffusion rate in the electrolyte solution inside a battery using the electric current reconstruction process. With respect to the changes in the internal state of the lithium-ion battery associated with cycle deterioration, we successfully visualized the part where the electrical conductivity has changed that is the deteriorated part causing the battery capacity to decrease inside the lithium-ion battery.

Multiscale Imaging Techniques for Real‐Time, Noninvasive Diagnosis of Li‐Ion Battery Failures

“…Accordingly, the expansion of the areas of abnormal conductivity was confirmed based on the changes in the uneven internal battery conductivity during cycling. [ 107 ] Brauchle et al of Daimler AG used an anisotropic magnetoresistive (AMR) sensor to measure the magnetic field surrounding a battery, as induced in the battery charge–discharge process, to visualize the current flow inside the batteries (Figure 12b). Notably, an AMR sensor generally has linear sensitivity across a wide range of magnetic fields, with the advantage of allowing the measurement and visualization of the magnetic field and current distribution, while avoiding the magnetic field shielding effect.…”

Diagnosis of Current Flow Patterns Inside Fault‐Simulated Li‐Ion Batteries via Non‐Invasive, In Operando Magnetic Field Imaging

“…To the best of our knowledge, MFI does not accurately measure the through-plane components of the reaction current (J) related to Li + transport between electrodes. [29,38] However, the reaction current (z component) can be disturbed by local defects in the active materials or electrolyte distribution, affecting the temporal electric current (x and y components) and allowing for the identification of abnormal current distributions in the failure spots. Utilizing machine learning techniques such as deep learning to train a 3D model can help separate overlap-ping 2D MFI data into individual layers.…”

Plasmonic Ag/Cu/PEG nanofluids prepared when solids meet liquids in the gas phase

“…3,4 The formation of finely dispersed metal NPs was observed, which gave an impetus to the development of this method. 5–12 A downside of the methodology is that only liquids with low equilibrium vapor pressure can be used; on the other hand, the method is beneficial in avoiding multiple chemical protocols and purification steps, providing a platform for ultra-pure metal/host medium interaction unmediated by the presence of linkers or chemical residues. Recently, more complex systems have been proposed that combine two or several metals to produce alloy nanofluids such as Ag–Au, 13–17 Pt–Au, 18–20 Pd–Au, 21,22 Au–Cu, 12,23,24 Ag–Pt, 9 Cu–Pt, 25 Ru–Au, Ru–Cu, Ru–Au–Cu, 12 and CoCrCuFeNi (high entropy alloy).…”