Cyber-Secured Battery Digital Twin for the Reliable Power Supply of Modern Telecommunication Networks
Telecom base stations rely on a continuous and stable power supply for efficient operation. Batteries are the preferred choice to provide backup power during outages. With recent advances in the Internet of Things (IoT) and communication networks, telecom base stations have become complicated cyber-physical systems. However, the sharing of control signals and security-critical information over communication channels makes battery energy storage systems (BESS) vulnerable to cyberattacks. To solve this vulnerability, this study explores the role of digital twins (DT) in detecting and mitigating cyberattacks. To this end, we propose a data-driven battery digital twin architecture to characterize battery performance over a wide range of operating temperatures and state-of-charge (SOC) levels. The digital twin leverages a feedforward neural network to estimate SOC from current, voltage, and temperature measurements. Stealthy attacks are formulated using a Soft Actor-Critic (SAC) reinforcement learning agent to corrupt current and voltage sensors, which affect the estimation of SOC. The attacks are detected by comparing the SOC estimated by the data-driven model and the Coulomb counting method.
