Resolving the LiFePO4 Battery State beyond Voltage-Based Diagnostics Using Surface-Scan MRI.

Authors

Konstantin Romanenko, Yanting Jin, Nikolai Avdievich

Published in

ACS applied materials & interfaces. Oct 12, 2026. Epub Oct 12, 2026.

Abstract

Reliable assessment of state of charge (SoC) and state of health (SoH) in commercial batteries remains a major challenge because their closed architecture obscures the underlying electrochemical, structural, and thermal processes, while existing diagnostic methods often provide only indirect, spatially averaged, voltage-based information about battery operation and degradation. Lithium iron phosphate (LFP) is among the most widely deployed cathode materials due to its low cost, long cycle life, and high thermal stability; however, its characteristically flat voltage profile fundamentally limits the information content of conventional diagnostics. In this work, we exploit the large magnetic-susceptibility contrast between the FP and LFP phases and the lithiation-dependent evolution of their phase fractions for accurate and time-efficient determination of SoC in commercial pouch cells and battery packs. Surface-scan magnetic resonance imaging (MRI) provides new SoC and SoH metrics through quantitative inside-out measurements of battery-induced magnetic field perturbations. LFP is a cathode material particularly well suited to this approach because its biphasic FP/LFP regime spans a large fraction of the usable SoC window while generating substantial magnetic susceptibility contrast between the FP and LFP phases. Under the biphasic approximation, the average magnetic susceptibility follows the lever rule, predicting a linear relationship between the induced magnetic field offset and SoC. Surface-scan MRI experiments confirm this prediction over a broad SoC range. We further establish a framework for spatial mapping of SoC and SoH heterogeneity in multi-cell architectures representative of practical battery packs. Despite minimal voltage differences between cells, the method enables quantitative visualization of charge imbalance and structural heterogeneity on the timescale of seconds.

PMID:
42860334
Bibliographic data and abstract were imported from PubMed on 12 Oct 2026.

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