Development of self-adaptive digital twin for battery monitoring and management system

The application of digital twin (DT) on battery energy storage systems (BESS) has attracted increasing attention in the last decade. However, existing studies usually focus on building pre-calibrated DT for state estimation and prediction. These DTs lack the ability for dynamic adaptation to changes in battery aging and evolving operating environment, which thus limits their effectiveness in intelligent decision-making for system performance enhancement. Therefore, this work develops a self-adaptive DT for battery monitoring and management system (DT-BMMS). The proposed self-adaptive algorithm ensures accurate long-term mapping between the physical entity and the digital model. Additionally, a model predictive control-based state-of-charge (SOC) balancing method is deployed. Simulation results demonstrate the capability of the developed DT-BMMS to adaptively adjust the DT as the system evolves, which allows the maintenance of SOC balancing under different scenarios.