Thermal fault diagnostics in battery systems are critical for ensuring safety, performance, and longevity, particularly in electric vehicle (EV) applications. Early detection of thermal anomalies such as hotspots or thermal runaway precursors can prevent catastrophic failures. Three primary methodologies are employed for this purpose: infrared thermography, embedded temperature sensors, and model-based approaches. Each technique offers unique advantages and is often used in combination to enhance diagnostic accuracy.

Infrared thermography is a non-contact method for measuring surface temperatures across a battery pack. It captures thermal images using infrared cameras, enabling the visualization of temperature distributions in real time. This approach is particularly effective for identifying localized hotspots, which may indicate internal short circuits, poor electrical connections, or uneven heat dissipation. For instance, a study on a 60 kWh EV battery pack revealed that infrared thermography detected a 15°C temperature gradient between adjacent cells, signaling an impending thermal event. The method’s spatial resolution allows for precise hotspot localization, but it is limited to surface measurements and may miss internal thermal anomalies without transparent or exposed cell surfaces.

Embedded temperature sensors, such as thermocouples, resistance temperature detectors (RTDs), and fiber optic sensors, provide direct internal monitoring of cell temperatures. These sensors are strategically placed within the battery pack, often near high-risk areas like cell interconnects or regions prone to mechanical stress. A case study involving a commercial 85 kWh EV battery pack demonstrated that embedded RTDs identified a gradual temperature rise of 0.5°C per minute in a single cell, preceding a thermal runaway event. While these sensors offer high accuracy and real-time data, their placement is critical; insufficient coverage may leave blind spots, and excessive wiring can complicate pack design. Fiber optic sensors, though costly, provide distributed temperature sensing with minimal electromagnetic interference, making them suitable for high-voltage environments.

Model-based approaches leverage mathematical models to infer thermal behavior and detect faults. Lumped-parameter models, which simplify the battery into thermal resistances and capacitances, are widely used due to their computational efficiency. These models predict temperature distributions based on input parameters such as current, voltage, and ambient conditions. Deviations between predicted and measured temperatures can indicate faults. For example, a lumped-parameter model applied to a 75 kWh EV battery pack detected a 10% discrepancy in heat generation rates, pinpointing a faulty cell with increased internal resistance. Advanced models incorporate electrochemical-thermal coupling to account for reaction heat and entropy changes, improving diagnostic sensitivity. However, model accuracy depends on parameter calibration, and unmodeled dynamics may lead to false positives or negatives.

Combining these methods enhances diagnostic robustness. A hybrid approach using infrared thermography for surface scans and embedded sensors for internal validation was tested on a 100 kWh battery pack. The system identified a latent thermal fault caused by a damaged separator, which was missed by individual methods. The thermography highlighted surface irregularities, while the sensors confirmed internal temperature spikes, and the model correlated these findings with abnormal heat generation rates.

Case studies from EV applications underscore the importance of early fault detection. In one incident, a prototype 90 kWh battery pack experienced thermal runaway during fast charging. Post-analysis revealed that infrared thermography had flagged a hotspot two minutes before the event, but the lack of embedded sensors delayed confirmation. Another study on a fleet of 50 EVs found that model-based diagnostics reduced false alarms by 30% compared to sensor-only systems, as the models accounted for normal operational variations like ambient temperature fluctuations.

Infrared thermography, embedded sensors, and model-based diagnostics each have limitations. Thermography requires line-of-sight access and may struggle with densely packed cells. Embedded sensors increase system complexity and cost. Models rely on accurate initial parameters and may struggle with novel fault modes. Despite these challenges, their integration provides a comprehensive solution for thermal fault detection.

Future advancements may focus on machine learning algorithms to analyze thermal data patterns and predict faults with higher precision. For now, the combination of empirical measurements and theoretical models remains the gold standard for ensuring battery safety. The EV industry continues to adopt these techniques, driven by the need to mitigate risks associated with high-energy-density batteries. Thermal fault diagnostics will remain a cornerstone of battery management as technology evolves toward higher performance and stricter safety standards.