US-20260126769-A1 - ADAPTIVE ENERGY MANAGEMENT FOR ENERGY-INTENSIVE ACTIONS

US20260126769A1US 20260126769 A1US20260126769 A1US 20260126769A1US-20260126769-A1

Abstract

An example operation includes one or more of determining a type of energy-consuming action being performed at a location by an energy-consuming system that is receiving energy from a remote energy service provider, receiving energy utilization data associated with the energy-consuming action from the remote energy service provider, determining a pattern of use of the type of energy-consuming action at the location, directing an amount of energy to be stored by an energy storage unit (ESU) at the location based on the energy utilization data and the pattern of use, and adjusting energy utilization of the ESU to locally supply the amount of energy to the system when the system is performing the energy-consuming action at a future point in time.

Inventors

Maximilian Parness
Norman Lu

Assignees

Toyota Motor North America, Inc.

Dates

Publication Date: 20260507
Application Date: 20241106

Claims (20)

1 . A method, comprising: determining a type of energy-consuming action being performed at a location by an energy-consuming system that is receiving energy from a remote energy service provider; receiving energy utilization data associated with the energy-consuming action from the remote energy service provider; determining a pattern of use of the type of energy-consuming action at the location; directing an amount of energy to be stored by an energy storage unit (ESU) at the location based on the energy utilization data and the pattern of use; and adjusting energy utilization of the ESU to locally supply the amount of energy to the energy-consuming system when the energy-consuming system is performing the energy-consuming action at a future point in time.
2 . The method of claim 1 , wherein the adjusting comprises limiting energy utilization by the energy-consuming system from the remote energy service provider and simultaneously increasing the energy utilization of the ESU by the energy-consuming system when performing the energy-consuming action at the future point in time.
3 . The method of claim 1 , comprising retrieving historical energy usage data of the location from a storage device, and executing an artificial intelligence (AI) model on the historical energy usage data to determine the pattern of use of the type of energy-consuming action at the location.
4 . The method of claim 1 , wherein the determining comprises determining an artificial intelligence (AI) process being performed locally by the energy-consuming system, the receiving comprises querying a remote server that processes the AI process for energy utilization data of the AI process at the remote server, and determining the amount of energy to be stored by the ESU based on the energy utilization data of the AI process.
5 . The method of claim 1 , wherein the directing comprises controlling a charging cycle of the ESU by at least one of increasing occurrences of the charging cycle and increasing a charging cycle time of the charging cycle to increase stored energy based on the pattern of use of the type of energy-consuming action at the location.
6 . The method of claim 1 , wherein the directing comprises establishing a communication channel between an energy panel at the location and a charging station of an electric vehicle at the location, and directing the charging station to increase energy stored in a rechargeable battery of the electric vehicle based on the energy utilization data and the pattern of use.
7 . The method of claim 1 , comprising detecting that the energy-consuming action consumes an amount of energy above a predefined threshold, wherein the receiving comprises querying the remote energy service provider for the energy utilization data in response to the amount of energy being above the predefined threshold.
8 . A system, comprising: a memory; and at least one processor that is communicably coupled to the memory, the at least one processor configured to: determine a type of energy-consuming action being performed at a location by an energy-consuming system that is receiving energy from a remote energy service provider; receive energy utilization data associated with the energy-consuming action from the remote energy service provider; determine a pattern of use of the type of energy-consuming action at the location; direct an amount of energy to be stored by an energy storage unit (ESU) at the location based on the energy utilization data and the pattern of use; and adjust energy utilization of the ESU to locally supply the amount of energy to the energy-consuming system when the energy-consuming system is performing the energy-consuming action at a future point in time.
9 . The system of claim 8 , wherein the at least one processor is configured to limit energy utilization by the energy-consuming system from the remote energy service provider and simultaneously increase the energy utilization of the ESU by the energy-consuming system when performing the energy-consuming action at the future point in time.
10 . The system of claim 8 , wherein the at least one processor is further configured to retrieve historical energy usage data of the location from a storage device, and execute an artificial intelligence (AI) model on the historical energy usage data to determine the pattern of use of the type of energy-consuming action at the location.
11 . The system of claim 8 , wherein the at least one processor is configured to determine an artificial intelligence (AI) process being performed locally by the energy-consuming system, query a remote server that processes the AI process for energy utilization data of the AI process at the remote server, and determine the amount of energy to be stored by the ESU based on the energy utilization data of the AI process.
12 . The system of claim 8 , wherein the at least one processor is configured to control a charging cycle of the ESU by at least one of increasing occurrences of the charging cycle and increasing a charging cycle time of the charging cycle to increase stored energy based on the pattern of use of the type of energy-consuming action at the location.
13 . The system of claim 8 , wherein the at least one processor is configured to establish a communication channel between an energy panel at the location and a charging station of an electric vehicle at the location, and direct the charging station to increase energy stored in a rechargeable battery of the electric vehicle based on the energy utilization data and the pattern of use.
14 . The system of claim 8 , wherein the at least one processor is further configured to detect that the energy-consuming action consumes an amount of energy above a predefined threshold, and query the remote energy service provider for the energy utilization data in response to the amount of energy being above the predefined threshold.
15 . A computer-readable storage medium that comprises instructions that when read by a processor cause the processor to perform: determining a type of energy-consuming action being performed at a location by an energy-consuming system that is receiving energy from a remote energy service provider; receiving energy utilization data associated with the energy-consuming action from the remote energy service provider; determining a pattern of use of the type of energy-consuming action at the location; directing an amount of energy to be stored by an energy storage unit (ESU) at the location based on the energy utilization data and the pattern of use; and adjusting energy utilization of the ESU to locally supply the amount of energy to the energy-consuming system when the energy-consuming system is performing the energy-consuming action at a future point in time.
16 . The computer-readable storage medium of claim 15 , wherein the adjusting comprises limiting energy utilization by the energy-consuming system from the remote energy service provider and simultaneously increasing the energy utilization of the ESU by the energy-consuming system when performing the energy-consuming action at the future point in time.
17 . The computer-readable storage medium of claim 15 , wherein the processor is further configured to perform retrieving historical energy usage data of the location from a storage device, and executing an artificial intelligence (AI) model on the historical energy usage data to determine the pattern of use of the type of energy-consuming action at the location.
18 . The computer-readable storage medium of claim 15 , wherein the determining comprises determining an artificial intelligence (AI) process being performed locally by the energy-consuming system, the receiving comprises querying a remote server that processes the AI process for energy utilization data of the AI process at the remote server, and determining the amount of energy to be stored by the ESU based on the energy utilization data of the AI process.
19 . The computer-readable storage medium of claim 15 , wherein the directing comprises controlling a charging cycle of the ESU by at least one of increasing occurrences of the charging cycle and increasing a charging cycle time of the charging cycle to increase stored energy based on the pattern of use of the type of energy-consuming action at the location.
20 . The computer-readable storage medium of claim 15 , wherein the directing comprises establishing a communication channel between an energy panel at the location and a charging station of an electric vehicle at the location, and directing the charging station to increase energy stored in a rechargeable battery of the electric vehicle based on the energy utilization data and the pattern of use.

Description

BACKGROUND Vehicles or transports, such as cars, motorcycles, trucks, planes, trains, etc., generally provide transportation to occupants and/or goods in a variety of ways. Functions related to vehicles may be identified and utilized by various computing devices, such as a smartphone or a computer located on and/or off the vehicle. SUMMARY The instant solution provides a method that includes one or more of determining a type of energy-consuming action being performed at a location by an energy-consuming system that is receiving energy from a remote energy service provider, receiving energy utilization data associated with the energy-consuming action from the remote energy service provider, determining a pattern of use of the type of energy-consuming action at the location, directing an amount of energy to be stored by an energy storage unit (ESU) at the location based on the energy utilization data and the pattern of use, and adjusting energy utilization of the ESU to locally supply the amount of energy to the energy-consuming system when the energy-consuming system is performing the energy-consuming action at a future point in time. The instant solution also provides a system that includes a memory communicably coupled to at least one processor, wherein the at least one processor is configured to one or more of determine a type of energy-consuming action being performed at a location by an energy-consuming system that is receiving energy from a remote energy service provider, receive energy utilization data associated with the energy-consuming action from the remote energy service provider, determine a pattern of use of the type of energy-consuming action at the location, direct an amount of energy to be stored by an energy storage unit (ESU) at the location based on the energy utilization data and the pattern of use, and adjust energy utilization of the ESU to locally supply the amount of energy to the energy-consuming system when the energy-consuming system is performing the energy-consuming action at a future point in time. The instant solution further provides a computer-readable storage medium comprising instructions, that when read by a processor, cause the processor to perform one or more of determining a type of energy-consuming action being performed at a location by an energy-consuming system that is receiving energy from a remote energy service provider, receiving energy utilization data associated with the energy-consuming action from the remote energy service provider, determining a pattern of use of the type of energy-consuming action at the location, directing an amount of energy to be stored by an energy storage unit (ESU) at the location based on the energy utilization data and the pattern of use, and adjusting energy utilization of the ESU to locally supply the amount of energy to the energy-consuming system when the energy-consuming system is performing the energy-consuming action at a future point in time. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1A is a diagram illustrating a process of identifying an energy-consuming activity within a location according to an example of the instant solution. FIG. 1B is a diagram illustrating a process of detecting the energy-consuming activity exceeds an energy threshold according to an example of the instant solution. FIG. 1C is a diagram illustrating a process of querying a server of a remote energy provider according to an example of the instant solution. FIG. 1D is a diagram illustrating a process of determining a future point in time in which the energy consuming activity consumes energy above a threshold according to an example of the instant solution. FIG. 1E is a diagram illustrating a process of instructing a local storage to store energy based on the future point in time according to an example of the instant solution. FIG. 1F is a diagram illustrating a process of controlling the flow of energy into the location during the energy-consuming activity according to an example of the instant solution. FIG. 2A illustrates a vehicle network diagram, according to an example of the instant solution. FIG. 2B illustrates another vehicle network diagram, according to an example of the instant solution. FIG. 2C illustrates yet another vehicle network diagram, according to an example of the instant solution. FIG. 2D illustrates a further vehicle network diagram, according to an example of the instant solution. FIG. 2E illustrates a flow diagram, according to an example of the instant solution. FIG. 2F illustrates another flow diagram, according to an example of the instant solution. FIG. 3A illustrates an Artificial Intelligence (AI)/Machine Learning (ML) network diagram for integrating an artificial intelligence (AI) model into any decision point in an example of the instant solution. FIG. 3B illustrates a process for developing an Artificial Intelligence (AI)/Machine Learning (ML) model that supports AI-assisted vehicle or occupant decision points. FIG.