Internal short circuits (ISC) in lithium-ion batteries present critical safety risks that demand advanced diagnostic techniques. Unlike external short circuits, ISCs develop gradually due to dendrite growth, separator degradation, or manufacturing defects, making them harder to detect before thermal runaway occurs. Specialized algorithms focus on three primary methods: voltage plateau analysis, ΔQ/dV methods, and internal temperature gradient monitoring. Each approach has distinct advantages and limitations in detection speed, sensitivity, and false alarm rates.

Voltage plateau analysis leverages the characteristic voltage behavior during charge and discharge cycles. Under normal conditions, a battery’s voltage curve follows predictable electrochemical reactions. However, an ISC introduces an anomalous plateau due to the parasitic current bypassing the active materials. The technique involves real-time monitoring of voltage deviations from baseline curves, with thresholds set to flag inconsistencies. For example, a plateau during constant-current charging that persists beyond expected time windows indicates potential ISC activity. The method’s effectiveness depends on the baseline accuracy and the severity of the short. Minor ISCs may produce subtle plateaus, requiring high-resolution voltage sensors (sub-millivolt precision) to avoid false negatives. However, overly sensitive thresholds increase false positives, especially in aged batteries where natural capacity fade can mimic ISC signatures.

ΔQ/dV methods analyze incremental capacity shifts by differentiating charge/discharge curves (dQ/dV) or comparing capacity differences (ΔQ) between cycles. An ISC causes a measurable divergence in these metrics due to charge loss through the short path. The algorithm calculates ΔQ by integrating current over time and comparing it to expected values derived from historical data. Similarly, dQ/dV plots reveal peak shifts or attenuation in phase transition regions, which correlate with ISC progression. This method excels in early-stage detection, as it captures minute capacity discrepancies before voltage anomalies become apparent. However, it requires high-fidelity current measurements and robust cycle-to-cycle data alignment. Noise in current sensors or operational variability (e.g., temperature fluctuations) can obscure true ΔQ signals, necessitating advanced filtering techniques. Tradeoffs arise between detection latency and noise tolerance: aggressive filtering speeds up detection but risks overlooking nascent ISCs, while conservative settings delay alerts but reduce false alarms.

Internal temperature gradient monitoring exploits localized heat generation at the ISC site. Unlike uniform heating from normal operation, an ISC creates asymmetric thermal profiles detectable with distributed temperature sensors. Fiber-optic sensors or multi-point thermocouples measure spatial gradients, with algorithms flagging deviations beyond predefined norms. For instance, a >2°C/mm gradient in a confined region may indicate ISC activity. This method is highly specific, as few failure modes produce such localized heating. However, its speed depends on thermal diffusion rates; slow-developing ISCs may not generate detectable gradients until significant damage occurs. Additionally, sensor placement is critical—poor coverage leaves blind spots, while excessive sensors increase system complexity. False alarms can stem from external thermal disturbances (e.g., uneven cooling) or sensor drift, requiring redundant measurements for validation.

The tradeoff between detection speed and false alarm rates is a central challenge. Fast detection prioritizes rapid response to mitigate hazards but often relies on lower thresholds or less data validation, increasing false positives. For example, voltage plateau analysis with a 50mV deviation threshold might trigger alerts within seconds but could misinterpret normal voltage noise as an ISC. Conversely, higher thresholds or time-averaging reduce false alarms but delay detection—a critical drawback for high-risk applications. ΔQ/dV methods face similar dilemmas: tight ΔQ tolerances enable early warnings but are prone to noise-induced errors, while relaxed tolerances miss early-stage ISCs. Temperature gradient monitoring balances this by combining spatial and temporal filtering, though at the cost of higher hardware overhead.

Algorithm fusion improves robustness by integrating multiple techniques. A hybrid approach might use ΔQ/dV for early detection, voltage plateaus for confirmation, and temperature gradients for localization. For instance, a preliminary ΔQ anomaly could trigger high-frequency voltage sampling to check for plateaus, followed by targeted thermal scans if both indicators align. This layered strategy reduces false alarms by cross-validating signals while maintaining rapid response. However, it demands significant computational resources and careful synchronization to avoid latency bottlenecks.

In practice, implementation depends on application constraints. Electric vehicles prioritize speed to prevent catastrophic failures, accepting higher false alarm rates (e.g., 1–5%) for rapid ISC identification. Grid storage systems, where operational continuity is critical, may favor precision over speed, tolerating longer detection times to minimize unnecessary shutdowns. Advances in edge computing and machine learning are narrowing these tradeoffs, enabling adaptive thresholds that adjust to real-time operating conditions.

Specialized ISC detection remains an evolving field, with ongoing research refining sensitivity and reliability. Future directions include embedding electrochemical impedance spectroscopy for real-time internal resistance tracking and leveraging AI to predict ISC likelihood from multidimensional data streams. These innovations aim to push detection boundaries while minimizing operational disruptions from false alarms.