It can issue AI early warnings for serious faults such as internal short circuits and thermal runaway of the battery, and conduct regular AI health assessments of battery safety to ensure the safety of energy storage.
Based on the big data of energy storage, the battery consistency coefficient is proposed, which can accurately calculate and evaluate the consistency level of the battery.
Follow the concept of the full life cycle of the battery, support battery traceability, and meet regulatory requirements; realize the black box function of energy storage safety accidents
Important battery performance parameters can achieve cell-level monitoring and prediction, accurately reflecting battery abnormalities.
It is applicable to multiple business scenarios such as energy storage stations, battery swap stations, photovoltaic-storage-charging stations, and power battery echelon utilization energy storage projects.
Support the synchronous online management of hundreds of GWh-level batteries; support the access and real-time online processing of multi-terminal data through Open API.
All-round three-dimensional information display of the earth, stations, equipment and modules.
The real scene is perfectly restored. It feels like being on the spot even when not.
Perfectly adapted to multiple scenarios and multiple devices.
Precisely locate fault work orders, and remote operation and maintenance is efficient and convenient.
Based on the AI big data algorithm, accurately predict the revenue of energy storage power stations
Alarm levels from level one to level four, closely monitor the safety of energy storage.