Three-dimensional thermal mapping is a critical aspect of battery thermal modeling and simulation, enabling precise analysis of heat distribution within battery cells, modules, and packs. Accurate thermal mapping ensures that potential hotspots are identified, thermal management systems are optimized, and safety risks are mitigated. Two primary techniques dominate this field: infrared thermography and embedded sensor networks. When combined with simulation validation, these methods enhance predictive accuracy and improve battery performance and longevity.
Infrared thermography is a non-contact method for capturing surface temperature distributions with high spatial resolution. By measuring infrared radiation emitted from a battery’s surface, thermal cameras generate detailed 2D temperature profiles. To extend this into a 3D thermal map, multiple cameras or angled imaging techniques are employed, reconstructing temperature gradients across complex geometries. This approach is particularly useful for identifying localized heating in large-format cells or multi-cell configurations. However, infrared thermography has limitations, including its inability to measure internal temperatures and its sensitivity to surface emissivity variations. Calibration against known references is necessary to ensure accuracy.
Embedded sensor networks provide direct internal temperature measurements, overcoming the limitations of surface-only techniques. Micro-thermocouples, fiber Bragg grating sensors, or resistive temperature detectors are strategically placed within the battery structure to capture real-time thermal data across multiple points. These sensors enable the construction of a 3D thermal map by interpolating between discrete measurement locations. High-density sensor arrays improve resolution but introduce challenges related to intrusiveness and potential interference with battery operation. Careful placement is required to minimize impact on mechanical integrity and electrochemical performance.
Integration of these thermal mapping techniques with simulation validation involves a closed-loop process. First, empirical data from infrared thermography or embedded sensors is used to calibrate and validate thermal models. Computational fluid dynamics (CFD) and finite element analysis (FEA) are common simulation tools that solve heat transfer equations across a discretized battery geometry. Input parameters such as thermal conductivity, specific heat capacity, and heat generation rates are derived from experimental measurements to ensure model fidelity.
Once validated, these simulations can predict thermal behavior under varying operational conditions, including charge-discharge cycles, environmental extremes, and fault scenarios. For example, a model calibrated with 3D thermal mapping data can simulate how a battery pack dissipates heat during fast charging, identifying regions prone to excessive temperature rise. This predictive capability allows for proactive design adjustments, such as optimizing cooling channel placement or modifying material properties.
A key advantage of combining empirical thermal mapping with simulation is the ability to extrapolate beyond measured conditions. While experimental data is limited to specific test cases, simulations can explore a broader parameter space, including scenarios that are difficult or dangerous to replicate physically. This is particularly valuable for assessing thermal runaway propagation, where direct testing poses significant safety risks. By leveraging validated models, researchers can evaluate mitigation strategies virtually before implementing them in hardware.
Challenges remain in achieving seamless integration between thermal mapping and simulation. Discrepancies often arise due to simplifications in model geometry, assumptions about material properties, or dynamic changes in battery behavior over time. Advanced techniques such as adaptive meshing and multi-physics coupling help address these issues by refining computational domains and accounting for interactions between thermal, electrical, and mechanical phenomena. Machine learning algorithms are increasingly used to enhance model accuracy by identifying patterns in large datasets and correcting for systematic errors.
Another consideration is the temporal resolution of thermal mapping. Batteries exhibit dynamic thermal behavior, with temperature fluctuations occurring over milliseconds during high-current events. High-speed infrared cameras or ultrafast response sensors are necessary to capture these transients accurately. Synchronization between data acquisition systems and simulation time steps is critical to ensure that dynamic effects are properly represented in the model.
The choice between infrared thermography and embedded sensors depends on the specific application. Infrared systems are ideal for rapid, non-invasive assessments during prototyping or quality control, while embedded sensors provide long-term monitoring capabilities for deployed systems. Hybrid approaches, where surface measurements are combined with sparse internal data, offer a balanced solution for comprehensive thermal characterization.
In summary, 3D thermal mapping techniques provide essential data for validating and refining battery thermal models. Infrared thermography offers high-resolution surface imaging, while embedded sensors deliver internal temperature profiles. When integrated with simulation tools, these methods enable predictive analysis of thermal behavior, supporting the development of safer and more efficient battery systems. Continued advancements in sensor technology, computational methods, and multi-physics modeling will further enhance the accuracy and utility of these approaches in battery design and optimization.