System and method for building an electric vehicle charging network

Patent metadata

Field	Value
Publication number	US-2025390644-A1
Application number	US-202519246470-A
Country	US
Kind code	A1
Filing date	Jun 23, 2025
Priority date	Jun 24, 2024
Publication date	Dec 25, 2025
Grant date	—

Abstract

In some embodiments, a disclosed method includes: storing, in a database, historical data associated with existing electric vehicle charging stations, identifying a first set of locations for potential electric vehicle charging stations, determining demand forecast associated with the set of locations based on the historical data, generating a score value for each location of the first set of locations, the score value being based on the demand forecast, calculating a loss value for a first location of the first set of locations based on one or more of a second location of the first set of locations and a plurality of locations of the existing electric vehicle charging stations, and generating a second set of locations for potential electric vehicle charging stations based on the demand forecast, the score value, and the loss value, the second set being a subset of the first set.

First claim

What is claimed is:

1 . A system, comprising:
a database storing historical data associated with existing electric vehicle charging stations; a computing device comprising at least one processor in communication with the database, the computing device being configured to: identify a first set of locations for potential electric vehicle charging stations; determine demand forecast associated with the set of locations based on the historical data; generate a score value for each location of the first set of locations, the score value being based on the demand forecast; calculate a loss value for a first location of the first set of locations based on one or more of a second location of the first set of locations and a plurality of locations of the existing electric vehicle charging stations; and generate a second set of locations for potential electric vehicle charging stations based on the demand forecast, the score value, and the loss value, the second set being a subset of the first set.

2 . The system of claim 1 , wherein the historical data includes electric vehicle population data, traffic data, charging station data, trip data, roadway data, or commercial data.

3 . The system of claim 1 , wherein the computing device is further configured to convert the demand forecast into a linear function to generate the score value.

4 . The system of claim 3 , wherein the computing device is further configured to generate a plurality of weights corresponding to a plurality of factors of the linear function.

5 . The system of claim 1 , wherein the loss value for the first location of the first set of locations is a share loss percentage corresponding to the second location of the first set of locations.

6 . The system of claim 1 , wherein the computing device is further configured to correlate a loss of demand to the plurality of locations of the existing electric vehicle charging stations to calculate the loss value for the first location of the first set of locations.

7 . The system of claim 1 , wherein the computing device is further configured to:
identify a plurality of route segments; and identify each route segment of the plurality of route segments corresponding to a location of the plurality of locations of the existing electric vehicle charging stations.

8 . A method comprising:
storing, in a database, historical data associated with existing electric vehicle charging stations; identifying a first set of locations for potential electric vehicle charging stations; determining demand forecast associated with the set of locations based on the historical data; generating a score value for each location of the first set of locations, the score value being based on the demand forecast; calculating a loss value for a first location of the first set of locations based on one or more of a second location of the first set of locations and a plurality of locations of the existing electric vehicle charging stations; and generating a second set of locations for potential electric vehicle charging stations based on the demand forecast, the score value, and the loss value, the second set being a subset of the first set.

9 . The method of claim 8 , wherein the historical data includes electric vehicle population data, traffic data, charging station data, trip data, roadway data, or commercial data.

10 . The method of claim 8 , further comprising converting the demand forecast into a linear function to generate the score value.

11 . The method of claim 10 , further comprising generating a plurality of weights corresponding to a plurality of factors of the linear function.

12 . The method of claim 8 , wherein the loss value for the first location of the first set of locations is a share loss percentage corresponding to the second location of the first set of locations.

13 . The method of claim 8 , further comprising correlating a loss of demand to the plurality of locations of the existing electric vehicle charging stations to calculate the loss value for the first location of the first set of locations.

14 . The method of claim 8 , further comprising:
identifying a plurality of route segments; and identifying each route segment of the plurality of route segments corresponding to a location of the plurality of locations of the existing electric vehicle charging stations.

15 . A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising:
storing, in a database, historical data associated with existing electric vehicle charging stations; identifying a first set of locations for potential electric vehicle charging stations; determining demand forecast associated with the set of locations based on the historical data; generating a score value for each location of the first set of locations, the score value being based on the demand forecast; calculating a loss value for a first location of the first set of locations based on one or more of a second location of the first set of locations and a plurality of locations of the existing electric vehicle charging stations; and generating a second set of locations for potential electric vehicle charging stations based on the demand forecast, the score value, and the loss value, the second set being a subset of the first set.

16 . The computer readable medium of claim 15 , wherein the instructions cause the at least one device to perform operations further comprising converting the demand forecast into a linear function to generate the score value.

17 . The computer readable medium of claim 16 , wherein the instructions cause the at least one device to perform operations further comprising generating a plurality of weights corresponding to a plurality of factors of the linear function.

18 . The computer readable medium of claim 15 , wherein the loss value for the first location of the first set of locations is a share loss percentage corresponding to the second location of the first set of locations.

19 . The computer readable medium of claim 15 , wherein the instructions cause the at least one device to perform operations further comprising correlating a loss of demand to the plurality of locations of the existing electric vehicle charging stations to calculate the loss value for the first location of the first set of locations.

20 . The computer readable medium of claim 15 , wherein the instructions cause the at least one device to perform operations further comprising:
identifying a plurality of route segments; and identifying each route segment of the plurality of route segments corresponding to a location of the plurality of locations of the existing electric vehicle charging stations.

Assignees

• Walmart Apollo Llc

Inventors

• Gupta Ashish
• Singh Mandeep
• Sharma Achala
• Ranjan Priyanshu
• Madbhavi Sujay Anand
• Pandey Abhijeet
• Gupta Kartikeya
• Jain Ankit Ajit
• Mishra Abhishek
• Ghosh Rahul
• Kapadia Vishal Narendra
• Lancelot Lloyd Andrew
• Rathi Manish Manoharlal
• Mantena Anup Varma
• Nelson Matthew Benjamin
• Daniel Lawrence Isaac
• Hostetler Ian Michael
• Fujii Yoshihisa

Classifications

• G06F2113/04

  Power grid distribution networks · CPC title

• G06F2111/06

  Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA] · CPC title

• G06Q50/06

  Energy or water supply · CPC title

• G06F2111/10

  Numerical modelling · CPC title

• G06F30/27 Primary

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title