Control and digitalization platform integrates data and AI technologies to enable real-time monitoring, coordinated control, and intelligent optimization of electrochemical systems. By building system-level digital twin models that map the physical system to its digital counterpart, it allows visualization, prediction, and optimization of operating states. Through unified control and data analysis of electrolysis, gas and liquid handling, thermal management, and energy systems, the platform ensures stable operation and enables adaptive adjustments under complex conditions, providing intelligent support for large-scale deployment and long-term operation.

It continuously captures and visualizes critical parameters during electrochemical system operation, forming the foundation for system control, optimization, and intelligent operation. By monitoring current, voltage, temperature, pressure, and flow in real time, a comprehensive dataset of system performance is established. In multi-module and complex operating conditions, the data system reflects operational status and trends, supporting control strategy adjustments, performance evaluation, and subsequent intelligent optimization, enabling the system to transition from “operable” to “understandable.”

It enables data-driven performance enhancement and predictive operation during system runtime, representing a key capability for electrochemical systems to advance from “controllable operation” to “intelligent operation.” Based on operational data and mechanistic models, we build a digital representation of the system (Digital Twin) to simulate and predict operating conditions. By integrating AI methods, operational strategies are continuously optimized. Under complex operating conditions and multi-system coupling, the system transitions from passive control to proactive optimization, improving overall efficiency and stability.

Electro Power Cell Energy Technology