Industry Background and Pain Points
As an important part of the new power system, commercial and industrial energy storage stations need to perform high-frequency acquisition of parameters such as voltage, current, temperature, and SOC for hundreds of battery clusters. At the same time, they must run energy dispatch algorithms, coordinate the collaborative operation of multiple systems including the BMS battery management system, PCS converter, fire protection system, and air conditioning system, and respond to grid dispatch commands to implement peak-valley arbitrage, demand response, backup power, and other functions. Traditional solutions generally use a three-system stitching architecture of “data acquisition gateway + PLC controller + industrial PC.” Data must be forwarded multiple times between devices, resulting in high system response latency, complex wiring, many failure points, and limited compute—making it difficult to run complex AI dispatch algorithms and battery state-of-health prediction models.
EA Series Product Functions and Application Solution
This solution adopts EA250Pro enhanced edge AI computer as the core control unit of the energy storage station EMS system. It is equipped with the NVIDIA Jetson Orin NX 16GB core module, providing 157 TOPS INT8 AI compute and an 8-core ARM Cortex-A78AE high-performance CPU. It integrates four major functions—data acquisition, logic control, AI computing, and cloud interaction—into one device, replacing the three systems of traditional solutions with a single unit.
Through optional Y-board IO expansion modules, the EA250Pro can flexibly expand multiple analog acquisition channels, isolated digital IO, multiple CAN FD interfaces, and RS485 interfaces. It can directly connect to battery cluster acquisition units, PCS converters, fire protection hosts, temperature control systems, and other equipment, achieving centralized acquisition and unified processing of plant-wide data. This eliminates intermediate gateway devices and greatly reduces wiring workload and failure nodes. The device runs high-precision SOC/SOH estimation algorithms and a reinforcement-learning-based peak-valley arbitrage energy dispatch model locally. By combining multi-dimensional data such as electricity price signals, load forecasts, and battery health status, it automatically optimizes charge/discharge strategies and responds to grid dispatch commands at millisecond speed, with dispatch accuracy and revenue capability significantly better than traditional rule-based strategies.
In terms of hardware reliability, the EA250Pro uses a 12V–24V wide-voltage power supply design with comprehensive power protection mechanisms. It has passed full EMC certification and can resist electromagnetic interference generated by high-power converters in energy storage stations. The device supports dual power supply redundant input, with seamless switching when one power source fails, ensuring the control system is not interrupted. At the network level, it supports dual Gigabit Ethernet redundancy and fiber expansion. It also has built-in multiple industrial protocols such as MQTT/OPC UA/Modbus and can seamlessly connect to various cloud platforms and local monitoring systems, enabling remote monitoring, parameter delivery, firmware upgrades, and other functions. To address energy storage safety requirements, the EA250Pro can connect to thermal imaging and smoke sensor data and run AI anomaly detection models to identify early signs of battery thermal runaway, triggering tiered warnings and fire protection linkage.
Customer Benefits and Deployment Results
A new energy integrator has adopted this solution in batches across multiple commercial and industrial energy storage projects. Hardware cost for a single station’s control system was reduced by 40%, on-site wiring workload decreased by 60%, and the commissioning cycle was shortened by half. Energy dispatch strategy response latency dropped from the second level in traditional solutions to the hundred-millisecond level, and comprehensive peak-valley arbitrage revenue increased by more than 15%. The local AI battery fault warning model can identify potentially failing battery clusters 7 days in advance, with warning accuracy reaching 92%, effectively reducing station safety risks. The device supports containerized deployment, allowing EMS applications, AI algorithms, and data acquisition services to run independently without interference, greatly improving system stability. It has accumulated over one million hours of stable operation and has received high recognition from customers.
