Internal short circuits in lithium-ion batteries are not instantaneous catastrophic events. They typically develop gradually through lithium dendrite growth, separator degradation, or contamination-induced conductive pathways. The resulting "soft" ISC manifests as an anomalous self-discharge phenomenon whose severity is directly proportional to the internal short circuit resistance — a quantity measurable long before thermal thresholds are breached.

Research from Pohang University of Science and Technology (2017) established that early detection of ISC resistance can prevent thermal runaway by providing actionable data while the fault is still in a reversible or manageable stage. Their 2020 follow-up demonstrated that soft ISC is a latent risk where conventional load-current-based methods fail due to insufficient persistent excitation, making the constant-current charging phase the optimal window for resistance estimation.

A 3D numerical simulation study from Dalian University of Technology (2022) demonstrated that the internal short circuit resistance — not the short circuit area, penetration depth, or geometric position — is the dominant variable governing thermal runaway propagation. Specifically, lower ISC resistance values produce far greater heat generation rates, confirming that resistance is both the most informative precursor signal and the physically correct quantity to monitor. This is consistent with NIST's materials measurement science frameworks for failure mode characterisation.

An in-operando study by Safion GmbH (2020) further demonstrated that fast impedance spectroscopy can capture real-time degradation effects at elevated temperatures, with the potential for direct integration into battery management systems (BMS). Grenoble Alpes University research (2020) showed that cell impedance, capacity, and contact resistance at the fault site collectively determine short-circuit current magnitude and heat generation rate — meaning that a multi-parameter impedance characterisation strategy is essential for accurate precursor identification. IEEE standards bodies increasingly reference multi-parameter approaches in battery safety guidelines.

ALGOLION LTD. is the most concentrated single assignee in the dataset, with at least seven active patent family members across US, EP, WO, IL, and IN jurisdictions (2015–2020). Their innovations consistently centre on stimulus-response characterisation of LIBs to extract hierarchical electrical response parameters for SCPC likelihood assessment. Their technology uniquely positions impedance-family measurements as predictive safety monitoring tools rather than diagnostic health metrics.

SAMSUNG ELECTRONICS CO., LTD. holds at least five active patent family members across US, EP, WO, and IN jurisdictions. Their strategy focuses on internal resistance change detection using existing BMS gauge data — maximising deployment reach by avoiding dedicated impedance hardware. Methods include Haar transform-based discontinuity detection and degradation parameter model comparison. This approach is consistent with IEC battery safety standards emphasising cost-effective large-scale deployment.

NISSAN MOTOR CO., LTD. holds at least three active US and EP patent families focused on solid-state secondary battery ISC prediction via AC impedance decomposition into electrolyte resistance and reaction resistance components.

Furukawa Automotive Systems Inc. (EP, 2024) identifies anomalous increases in charge current over time as a precursor signal — complementing impedance spectroscopy with simpler current-monitoring approaches suitable for cost-constrained automotive deployments. Huawei Technologies Co., Ltd. (EP, 2024) introduced a cross-cell voltage comparison methodology that qualitatively identifies ISC by comparing voltage changes of target and reference cells across multiple charging cycles. The PatSnap customer success library documents how automotive and electronics firms use these IP landscapes to accelerate R&D decisions. For developer access to the underlying patent data, see PatSnap's open API.