US 2026/0018889 A1 · INTELLIGENT RELAY-BASED LOAD MANAGEMENT SYSTEM WITH MACHINE LEARNING OPTIMIZATION AND MOBILE APPLICATION CONTROL FOR BATTERY ENERGY STORAGE SYSTEMS

Abstract

A load management system integrates comparator-based neutral sensing, machine learning prediction, and relay control into a single integrated “AC” board requiring no additional wiring. A highspeed comparator circuit detects grid failures in sub millisecond timeframes, providing clean data to a temporal convolutional network that predicts load requirements 24 hours in advance with integration of external data sources such as weather and time of use pricing. The system automatically manages 120V and 240V circuits during grid transitions, learning from user override patterns to continuously improve performance. A mobile application provides real-time monitoring and control. The integration of low-latency sensing with predictive machine learning enables performance improvements exceeding 40% in battery runtime compared to conventional systems, while reducing installation time and cost.

Claims20

Dependent claims

A load management system for battery energy storage systems comprising:

a comparator-based neutral sensing circuit that generates a grid status signal with a response time of less than one millisecond;
a machine learning processor that receives said grid status signal and executes a temporal convolutional network algorithm to generate a plurality of predictive load requirements;
and
three relay switches, a first relay switch that controls a connection to a power grid, a second relay switch that controls a 120V circuit, and a third relay switch controls a 240V circuit;
wherein each of said three relay switches has an open position and a closed position;
wherein said machine learning processor predictively controls said three relay switches based on said plurality of predictive load requirements and said grid status signal, such that said third relay switch is automatically put into said open position during off-grid transitions while maintaining said second relay switch in said closed position.

3 more elements

An intelligent relay-based load management system with machine learning optimization and mobile application control for battery energy storage systems comprising:

an AC board;
and
a user device that is in wireless communication with said AC board;
wherein said AC board comprises:
a comparator circuit that monitors a power system electrically connected to said AC board;
a microprocessor that executes machine learning algorithms;
three relay switches for load control;
and
an autotransformer;
wherein said comparator circuit provides input data to said machine learning algorithms with a latency that is less than 100 microseconds, enabling said machine learning algorithms to detect patterns in grid behavior and predict load requirements with an accuracy of at least 85%;
wherein said three relay switches comprise a first relay switch that controls a connection to a power grid, a second relay switch that controls a 120V circuit, and a third relay switch controls a 240V circuit;
wherein each of said three relay switches has an open position and a closed position;
wherein said autotransformer is positioned between said second and third relay switches that provides voltage balancing and soft-start capability;
wherein said user device comprises an application that has a graphical user interface;
and
wherein said graphical user interface enables real-time user override of said relay switch positions.

13 more elements

Dependencies are inferred from the claim text.

Intelligent relay-based load management system with machine learning optimization and mobile application control for battery energy storage systems

Life of this patent

Intelligent relay-based load management system with machine learning optimization and mobile application control for battery energy storage systems

Life of this patent

Abstract

Claims20