Transient Thermal Response Mapping in Prismatic Cells Under Pulsed Charging Using Embedded Sensor Arrays
Year: 2025
Type: article
Abstract: Fast charging protocols for lithium-ion batteries have introduced significant thermal challenges, especially for prismatic cell formats used in electric vehicles and grid storage. Understanding transient thermal response during pulsed charging is critical for ensuring safe operation and extending battery life. This study presents a high-resolution thermal characterization technique using embedded sensor arrays within commercial-grade prismatic lithium-ion cells to track real-time heat generation and dissipation patterns under pulsed current profiles. Cells were charged using intermittent high-current pulses (up to 4C) with rest periods, emulating real-world fast-charging scenarios. Thermocouple and thin-film thermistor arrays were strategically embedded between electrode layers and beneath cell tabs to capture localized temperature variations. Results showed rapid thermal spikes exceeding 50°C within milliseconds of current onset, followed by non- linear cooling curves, highlighting thermal inertia in cell construction. The spatiotemporal temperature data was further used to derive effective thermal conductivity and to calibrate a multi-layered lumped thermal model of the prismatic structure. Insights revealed poor heat dispersion near tab areas and asymmetrical thermal gradients, emphasizing the need for improved tab cooling and internal layout optimization. This approach allows for predictive diagnostics, as recurring thermal anomalies were found to align with early signs of localized impedance growth and lithium plating. The methodology is applicable for validating battery thermal models and for benchmarking various cell designs under rapid charging conditions. By incorporating thermal sensors directly within cells, the study demonstrates a new standard for internal temperature profiling that enables safer and more efficient energy storage systems.
Author: Chimezie Oguike
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930833342
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