A multi-agent shared machine learning approach for real-time battery operation mode prediction and control

A method, system, and device for controlling energy storage devices are provided, the method including receiving a trained machine learning model from a centralized machine learning system, recording temporal data for a respective energy storage device, periodically transmitting the temporal data to the machine learning system, performing a mode prediction for controlling the energy storage device using the trained machine learning model and the temporal data, and sending a control signal to the energy storage device to operate in the predicted mode. The machine learning system aggregates the temporal data transmitted by each agent and uses the aggregated temporal data to update the machine learning model. By using aggregated temporal data, less data is needed from an individual energy storage device so that when a new energy storage device joins the machine learning system, the new energy storage device can benefit from increased performance with less computation.