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Journal Articles Journal of Magnetic Resonance Open Year : 2022

## Numerical modeling of Surface-Scan MRI experiments for improved diagnostics of commercial battery cells

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Konstantin Romanenko
• Function : Author
• PersonId : 1141792
Alexej Jerschow
• Function : Author

#### Abstract

Recent progress in MRI methods development led to a novel concept for operando screening of commercial battery cells. Sensing electrochemical processes inside an operating cell can be done via the detection of associated magnetic fields. This concept is based on the classic phenomenon described by Ampère's circuital and Biot-Savart laws. A new method referred to as Surface-Scan MRI employs ultra-fast quantitative mapping of the magnetic field in a thin layer of polymer placed in direct contact with the cell. Preliminary experimental work demonstrated the ability of Surface-Scan MRI to detect cells that undergone overcharging, a hazardous event that can degrade cells' components and ultimately creates a risk of fire. In the future, the analysis of Surface-Scan MRI data can rely largely on numerical simulations of electromagnetic phenomena based on accurate models of real cells. A series of numerical tests performed in this work confirms the capacity of Surface-Scan MRI to detect a variety of defects associated with electrochemical degradation of cathode, anode and electrolyte materials. Effects of morphology, location and conductivity of the defects on magnetic field distributions were examined for realistic cell models including flat jelly roll configurations. MRI resolution limits associated with the defect domain size and with the distance between the defect and the detection medium were estimated. Location and morphology of highly conductive defects ($\sigma$ > 10$^5$ S m$^{-1}$), e.g. regions rich in quasi-metallic dendrites, can be directly identified from Surface-Scan MRI images. Low conductivity defects , e.g. degraded electrolyte or cathode and anode coatings, manifest via patterns much larger than the defects.

### Dates and versions

cea-03654405 , version 1 (28-04-2022)

### Licence

Attribution - NoDerivatives - CC BY 4.0

### Identifiers

• HAL Id : cea-03654405 , version 1
• DOI :

### Cite

Konstantin Romanenko, Alexej Jerschow. Numerical modeling of Surface-Scan MRI experiments for improved diagnostics of commercial battery cells. Journal of Magnetic Resonance Open, 2022, 10-11, pp.100061. ⟨10.1016/j.jmro.2022.100061⟩. ⟨cea-03654405⟩

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