Single-Sensor Spatial Temperature Estimation for Pouch-Type Lithium-Ion Batteries Using Inverse Heat Transfer Analysis

Precise thermal management of pouch-type lithium-ion batteries is critical to ensuring operational safety in electric vehicles (EVs). Conventional studies, which typically rely on lumped parameter models, are limited in predicting localized cell temperatures because they ignore surface temperature gradients caused by internal non-uniformities. This study proposes a real-time temperature distribution prediction method using a single reference sensor and a spatial thermal characteristic map optimized for on-board Battery Management Systems (BMS). Using Inverse Heat Transfer Analysis (IHTA), localized entropic coefficients were extracted from six points, reducing experimental time by 95% compared to conventional potentiometric methods. To ensure computational efficiency for real-time applications, these parameters were integrated into a spatial map via a first-order linear model, enabling continuous State of Charge (SOC)-based estimation. Validation on high-capacity pouch cells confirmed high accuracy, with a Root Mean Square Error (RMSE) below 0.7°C and a maximum absolute error under 3.5°C at unmeasured locations. This approach minimizes sensor requirements and complexity, providing a scalable foundation for future expansion into module and pack-level thermal monitoring.