A Hierarchical and Self-Evolving Digital Twin (HSE-DT) Method for Multi-Faceted Battery Situation Awareness Realisation

Accurate battery state estimation is important for the operation of energy storage systems, yet existing methods struggle with the complexity and dynamic nature of battery conditions. Conventional techniques often fail to extract relevant spatial and temporal features from basic battery data effectively, leading to insufficient situational awareness in battery management systems. To address this gap, we propose a Hierarchical and Self-Evolving Digital Twin (HSE-DT) method that enhances battery state estimation by coordinating multiple estimation techniques in a hierarchical framework and enabling adaptive updating through transfer learning. The model integrates a Transformer-Convolutional Neural Network (Transformer-CNN) architecture to process historical and real-time data, capturing dynamic state variations with high precision. Simulations indicate that the values of root mean square error (RMSE) for state of charge (SOC) and state of health (SOH) are lower compared to other algorithms, being less than 0.9% and 0.8%, respectively. Its hierarchical structure allows the integration of different estimation models, and the self-evolving method allows the method to adapt to changes in different operating conditions. The experimental results show that the method can estimate the battery state with high accuracy and stability, thus enhancing multi-faceted situational awareness.