The development of Digital Twin (DT) technology in Battery Management Systems (BMSs) presents a transformative approach for maintenance, monitoring, and predictive diagnostics, especially in the demanding field of space applications. DTs, through their three-layer structure, provide an accurate and dynamic virtual representation of the physical entity, continuously updated via bidirectional data exchange provided by the communication link. Given the promising capabilities of the DT approach in real-time applications, its integration into BMSs is straightforward, as it can enhance monitoring and prediction of nonlinear electrochemical systems, such as space-grade lithium-ion batteries, supporting the mitigation of ageing effects under the unique constraints of the space environment. Despite notable progress in BMS technologies, the choice of estimation techniques consistent with the DT paradigm remains insufficiently defined. This survey examines the state of the art with the aim of bridging the conceptual framework of DTs and existing battery management algorithms, identifying the methodologies most suitable in accordance with DT architectures and principles. The scope of this paper is to provide researchers and engineers with a comprehensive overview of the advancements, key enabling technologies, and implementation strategies for Digital Twins in space BMSs, ultimately contributing to more reliable and efficient space missions.

Purpose: In this study, a methodology for damage detection and localization in aeronautical structures based on Automatic Operational Modal Analysis and a strain-based damage index is presented. Methods: The proposed approach enables the automatic extraction of strain mode shapes under operational conditions, relying solely on output measurements. Modal parameters are estimated using Stochastic Subspace Identification, with pole selection performed via Density-Based Spatial Clustering. The methodology is initially validated numerically on two different case studies: a stiffened cantilevered plate and a composite glider. In both cases, damage is simulated by locally reducing the stiffness of specific regions, and strain signals are collected from virtual sensors under spatially and temporally random excitation. The proposed approach is then applied to an experimental test on a manufactured composite glider model, instrumented with Fiber Bragg Grating sensors bonded to the wing surface. Results and Conclusion: The numerical results demonstrate that the proposed methodology effectively detects and localizes damage across varying intensities, even under noisy conditions. Experimental results confirm the effectiveness of the methodology in real-world conditions, highlighting its potential for in-flight Structural Health Monitoring applications.

The increasing reliance of modern society on space-based systems makes robust monitoring and prediction essential to guarantee service continuity. In particular, critical subsystems such as energy storage devices undergo strongly nonlinear degradation and must be monitored accurately under the harsh environment and highly dynamic operating profiles typical of space missions, which make State of Health (SOH) estimation especially challenging. This highlights the need for approaches tailored to space applications. To address these challenges, this study proposes a partialization technique that recreates operational conditions using SOH-labelled laboratory datasets. These partialized curves are then used to train and evaluate four supervised machine-learning models with progressively higher complexity. The models are assessed under the same operating conditions, under different conditions to test generalization, and in a deployment-oriented scenario that compares accuracy against computational cost. The proposed framework helps quantify both the promise and the current limitations of data-driven SOH estimation on space-like profiles, underscoring the importance of matching laboratory datasets to real operational behavior. Overall, it provides a practical benchmark for evaluating ML-based SOH estimation beyond ideal lab settings and represents a step toward deployment in real space missions.