Energies, Vol. 18, Pages 5858: Digital Twins for Space Battery Management Systems: A Comprehensive Review of Different Approaches for Predictive Maintenance and Monitoring

Energies, Vol. 18, Pages 5858: Digital Twins for Space Battery Management Systems: A Comprehensive Review of Different Approaches for Predictive Maintenance and Monitoring

Energies doi: 10.3390/en18215858

Authors:
Roberto Giovanni Sbarra
Michele Pasquali
Giuliano Coppotelli
Paolo Gaudenzi
Davide di Ienno
Carlo Ciancarelli
Niccolò Picci

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.

More From Author

Energies, Vol. 18, Pages 5860: Multi-Step Sky Image Prediction Using Cluster-Specific Convolutional Neural Networks for Solar Forecasting Applications

Energies, Vol. 18, Pages 5857: Gas Evolution and Stability of Alkali-Activated MSWI Slag and Fly Ash: Implications for Safe Use and Energy Valorization

Leave a Reply

Your email address will not be published. Required fields are marked *