A cloud based Digital Twin architecture for battery management systems with edge integration and advanced sensing

Conventional battery management systems are commonly implemented on micro-controller based platforms with limited computational and storage resources. These constraints can result in delayed or inaccurate condition monitoring and hinder the deployment of advanced models for performance optimization. This paper proposes a cloud-based Digital Twin architecture for battery management systems to overcome the limitations of conventional implementations. The proposed approach leverages high-performance computing resources to enable large-scale data processing, long-term storage, and the execution of computationally intensive data-driven algorithms. The architecture is organized into physical, edge, and cloud layers, which together enable real-time, bidirectional data exchange with the physical battery system. The edge layer acts as an intermediary between the battery management systems hardware and the cloud, transmitting sensor data including voltage, current, temperature, impedance, pressure, and acoustic measurements. In addition, the availability of historical battery data in the cloud enables data-driven algorithms to infer the health and operational state of individual cells and modules. The proposed architecture enables a more accurate and comprehensive understanding of the real-time operating conditions of battery cells, modules, and packs.