Abstract: Accurate real-time temperature prediction in electrochemical energy storage systems plays a critical role in enhancing battery performance, extending lifespan, and preventing thermal runaway, a major safety concern. Proper thermal management ensures uniform heat distribution, which is essential for optimizing efficiency, safety, and reliability. However, obtaining comprehensive real-time temperature data for large-scale battery systems is challenging due to the high costs, complexity, and impracticality of deploying extensive sensor networks. This challenge highlights the need for data-driven methods to infer complete temperature fields from sparse measurements, addressing the inverse problem of temperature reconstruction. This study presents a data-driven approach using the gappy proper orthogonal decomposition (Gappy POD) algorithm, a reduced-order modeling technique, for real-time temperature monitoring of large-scale battery modules. Gappy POD is particularly well-suited for scenarios with limited sensor data, as it leverages spatial correlations to reconstruct the full temperature field. The proposed methodology is validated through experimental data collection and numerical simulations of large-format prismatic battery modules, which capture the thermal behavior of the battery thermal management system and support the development of a digital twin of the energy storage system. To maximize data efficiency and minimize computational costs, we employ the Latin hypercube sampling (LHS) method to design a small but representative temperature database. This database captures essential thermal characteristics of the battery module, allowing for accurate temperature predictions without exhaustive simulations. The Gappy POD algorithm is further enhanced by incorporating a correlation coefficient filtering technique, which identifies a minimal set of optimal measurement points to ensure high accuracy in the reconstructed temperature field while reducing sensor requirements. In our experiments, we used temperature data from only eight surface measurement points to reconstruct the temperature distribution across 48 battery cells, covering 240 internal and external temperature points. The results show that the reconstructed temperature profiles closely matched actual data, demonstrating the effectiveness of the Gappy POD algorithm. The reconstructed temperature curves showed a strong temporal correlation with the measured data, even under varying conditions. However, near the liquid cooling plate, where temperature gradients are more nonlinear, reconstruction error increased slightly. Despite this, the maximum absolute error remains within 0.3 K, highlighting the robustness of the method. This slight error increase is attributed to the complex heat transfer dynamics near cooling interfaces, which pose challenges for most data-driven models. This study highlights the effectiveness of the Gappy POD algorithm in managing the thermal dynamics of large-scale energy storage systems in real time. By minimizing the need for extensive sensor networks and reducing computational costs, it provides a resource-efficient solution for accurate temperature monitoring and control. The integration with a small-sample database further enhances its applicability to large-scale systems. These insights pave the way for developing digital twins, which facilitate predictive maintenance, fault detection, and optimized operational strategies. Accurate temperature reconstruction is crucial for building these digital twins, providing a solid foundation for their future deployment. Integrating Gappy POD with other models can improve the overall efficiency, safety, and reliability of energy storage systems, driving advancements in smart and sustainable energy management solutions.