IP Library › Granted Patent US 12,479,324
Granted Patent B2
US 12,479,324 · App. 17/257,298 · Granted Nov 25, 2025

Automatic routing through electric vehicle charging stations

Inventors: Alex Donaldson (Mountain View, CA); David X. Wang (Arncliffe, AU); Kostas Kollias (Mountain View, CA); Xin Wei Chow (Mountain View, CA); Navin Gunatillaka (Mountain View, CA); Jesse Head (Mountain View, CA); Michael Graham Woodward (Ultimo, AU); Ingrid Trollope (Mountain View, CA); Andrew Foster (Naremburn, AU); Ivan Kuznetsov (Mountain View, CA); Sreenivas Gollapudi (Mountain View, CA); Christine Nguyen (Mountain View, CA); Kyle Morgan (Mountain View, CA)
Assignee: Google LLC
B60L53/65G01C21/3469B60L2240/62B60L2240/70
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,479,324
App. No.
17/257,298
Granted
Nov 25, 2025
Kind
B2
Abstract

To navigate an electric vehicle from a starting location to a destination, a system identifies multiple charging stations between the starting location and the destination and determining a navigation route that requires a least amount of time for the electric vehicle to travel from the starting location to the destination via one or more of the charging stations, including determining a non-linear relationship between an amount of time and an amount of charge the EV receives during the amount of time.

Claims (63)

1 . A method for navigating an electric vehicle (EV) from a starting location to a destination, the method comprising:

determining a first non-linear relationship between an amount of time and an amount of charge the EV receives during the amount of time, the non-linear relationship being based on an EV charge profile defining a charging speed as a function of the charge of the EV;

identifying, by processing hardware, a plurality of charging stations between the starting location and the destination;

determining, by the one or more processors, a second non-linear relationship between an amount of time and an amount of charge provided by one of the plurality of charging stations, the second non-linear relationship being based on a station charge profile defining the charging speed as a function of the percent of charge provided by the one of the plurality of charging stations;

determining, by the processing hardware, a navigation route that requires a least amount of time for the (EV) to:

(i) travel from the starting location to the destination, and

(ii) to charge at each of the charging stations, based on the comparing the EV charge profile with the station charge profile for each of the plurality of charging stations; and

providing, by the processing hardware, a set of navigation instructions for traveling from the starting location to the destination along the navigation route for display on a user interface that causes the EV to be controlled in accordance with the navigation instructions to travel to the destination along the navigation route, including two or more stops at the two or more charging stations.

2 . The method of claim 1 , wherein determining the navigation route includes:

constructing a navigation graph in which at least some of the plurality of charging stations define respective nodes, and routes between the charging stations define edges.

3 . The method of claim 2 , wherein constructing the graph includes:

constructing an initial highly connected graph in which each of the plurality of charging stations represents a respective node, with N edges interconnecting the plurality of nodes;

identifying a minimum spanning tree (MST) of the initial highly connected graph; and

generating the navigation graph by adding some but not all of the N edges of the initial highly connected graph to the MST.

4 . The method of claim 2 , further comprising:

generating, for at least some of the plurality nodes, a representation of the corresponding charging station as a bipartite graph, in which:

a first set of sub-nodes represents amounts of charge upon entering the charging station,

a second set of sub-nodes represents amounts of charge upon exiting the charging station, and

edges between sub-nodes in the first set and sub-nodes in the second set define time delays associated with the corresponding increase in charge.

5 . The method of claim 2 , wherein constructing the navigation graph includes selecting, within the plurality of charging stations, charging stations compatible with the EV.

6 . The method of claim 2 , wherein constructing the navigation graph includes:

excluding from the navigation graph charging stations with a trust score below a trust threshold value.

7 . The method of claim 2 , further comprising pre-computing the navigation graph prior receiving a request for navigation directions.

8 . The method of claim 2 , further comprising:

generating a first node to represent the starting location;

generating a second node to represent the destination; and

connecting the first node and the second node to the graph.

9 . The method of claim 2 , wherein:

determining the navigation route includes applying an A* search algorithm to the navigation graph.

10 . The method of claim 1 , further comprising:

providing an option for a user to select an ecological route; and

generating the ecological route that optimizes energy consumption.

11 . The method of claim 10 , wherein generating the ecological route includes avoiding changes in elevation.

12 . The method of claim 10 , further comprising: generating the user interface screen, including applying levels of highlighting to different segments in accordance with a state of the charge of a battery expected for the segment.

13 . The method of claim 10 , further comprising:

receiving real-time data indicative of availability of ports at the daisy chain of charging stations; and

automatically updating the navigation route in view of the received real-time data.

14 . A system comprising: one or more processors; and

a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the system to:

determine a first non-linear relationship between an amount of time and an amount of charge the electric vehicle (EV) receives during the amount of time, the non-linear relationship being based on an EV charge profile defining a charging speed as a function of the charge of the EV;

identify a plurality of charging stations between the starting location and the destination;

determine a second non-linear relationship between an amount of time and an amount of charge provided by one of the plurality of charging stations, the second non-linear relationship being based on a station charge profile defining the charging speed as a function of the percent of charge provided by the one of the plurality of charging stations;

determine a navigation route that requires a least amount of time for the to:

(i) travel from the starting location to the destination, and

(ii) charge at each of the charging stations, based on the comparing the EV charge profile with the station charge profile for each of the plurality of charging stations; and

provide a set of navigation instructions for traveling from the starting location to the destination along the navigation route for display on a user interface that causes the EV to be controlled in accordance with the navigation instructions to travel to the destination along the navigation route, including two or more stops at the two or more charging stations.

15 . The system of claim 14 , wherein to determine the navigation route, the instructions cause the system to:

construct a navigation graph in which at least some of the plurality of charging stations define respective nodes, and routes between the charging stations define edges.

16 . The system of claim 15 , wherein to construct the graph, the instructions cause the system to:

construct an initial highly connected graph in which each of the plurality of charging stations represents a respective node, with N edges interconnecting the plurality of nodes;

identify a minimum spanning tree (MST) of the initial highly connected graph; generate the navigation graph by adding some but not all of the N edges of the initial highly connected graph to the MST.

17 . The system of claim 15 , wherein the instructions further cause the system to:

generate, for at least some of the plurality nodes, a representation of the corresponding charging station as a bipartite graph, in which:

a first set of sub-nodes represents amounts of charge upon entering the charging station,

a second set of sub-nodes represents amounts of charge upon exiting the charging station,

and

edges between sub-nodes in the first set and sub-nodes in the second set define time delays associated with the corresponding increase in charge.

18 . The system of claim 15 , wherein to construct the graph, the instructions cause the system to:

select, within the plurality of charging stations, charging stations compatible with the EV.

19 . The system of claim 15 , wherein to construct the graph, the instructions cause the system to:

exclude from the navigation graph charging stations with a trust score below a trust threshold value.

20 . The system of claim 15 , wherein the instructions cause the system to:

pre-compute the navigation graph prior receiving a request for navigation directions.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 16, 2025
From: MORGAN, KYLE
To: GOOGLE LLC
Reel/Frame 069893/0218 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 3, 2022
From: NGUYEN, CHRISTINE
To: GOOGLE LLC
Reel/Frame 060703/0681 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 4, 2021
From: DONALDSON, ALEX; WANG, DAVID X.; KOLLIAS, KOSTAS; CHOW, XIN WEI; GUNATILLAKA, NAVIN; HEAD, JESSE; WOODWARD, MICHAEL GRAHAM; TROLLOPE, INGRID; FOSTER, ANDREW; KUZNETSOV, IVAN; GOLLAPUDI, SREENIVAS
To: GOOGLE LLC
Reel/Frame 057073/0832 →
Continuity (1)
Related Publication 20230211692A1 · Jul 6, 2023
References Cited (39)
US 6021372A · Harrington · 2000 [cited by applicant]
US 11397092B2 · DeLuca · 2022 [cited by examiner]
US 11430335B2 · Elisha · 2022 [cited by examiner]
US 11584248B2 · Yu · 2023 [cited by examiner]
US 20100094496A1 · Hershkovitz · 2010 [cited by examiner]
US 20120089329A1 · Kim et al. · 2012 [cited by applicant]
US 20120109519A1 · Uyeki · 2012 [cited by examiner]
US 20140025226A1 · Brown · 2014 [cited by examiner]
US 20140046595A1 · Segawa · 2014 [cited by examiner]
US 20140172298A1 · Guo et al. · 2014 [cited by applicant]
US 20160176394A1 · Geller · 2016 [cited by examiner]
US 20160380440A1 · Coleman, Jr. · 2016 [cited by examiner]
US 20170138750A1 · Weber · 2017 [cited by examiner]
US 20170168493A1 · Miller · 2017 [cited by examiner]
US 20190217735A1 · Donnelly · 2019 [cited by examiner]
US 20190308510A1 · Beaurepaire · 2019 [cited by examiner]
US 20190316924A1 · Morgan-Brown · 2019 [cited by examiner]
US 20200089241A1 · Kao · 2020 [cited by examiner]
US 20200333148A1 · Qiu · 2020 [cited by examiner]
US 20210034795A1 · Zhang · 2021 [cited by examiner]
US 20210318685A1 · Jenkins · 2021 [cited by examiner]
US 20210325891A1 · Young · 2021 [cited by examiner]
US 20210333114A1 · Roggenkamp · 2021 [cited by examiner]
US 20210389144A1 · Kim · 2021 [cited by examiner]
US 20210389145A1 · Liu · 2021 [cited by examiner]
US 20220355696A1 · Ahtikari · 2022 [cited by examiner]
EP 2645062A2 · 2013 [cited by applicant]
JP 2012019627A · 2012 [cited by applicant]
JP 2013148379A · 2013 [cited by applicant]
WO WO2016020997A1 · 2016 [cited by applicant]
WO WO2016093118A1 · 2016 [cited by applicant]
WO WO2018180583A1 · 2018 [cited by applicant]
International Search Report and Written Opinion for Application No. PCT/US2020/049174, dated May 28, 2021. [cited by applicant]
Pourazarm et al., “Optimal Routing of Electric Vehicles in Networks with Charging Nodes: a Dynamic Programming Approach,” IEEE International Electric Vehicle Conference (2014). [cited by applicant]
Zhang et al., “Effective Charging Planning Based on Deep Reinforcement Learning for Electric Vehicles,” IEEE Transactions on Intelligent Transportation Systems, 22(1):542-554 (2020). [cited by applicant]
Japanese Patent Application No. 2022-525409, Notice of Reasons for Rejection, mailing date of Jul. 29, 2024. [cited by applicant]
First Chinese Office Action for Application No. 202080076521.9, dated Jan. 17, 2025. [cited by applicant]
Office Action for Korean Patent Application No. 10-2022-7009692 dated Aug. 5, 2025. 11 pages. [cited by applicant]
Office Action for Chinese Patent Application No. 202080076521.9 dated Sep. 27, 2025. 4 pages. [cited by applicant]