IP Library Granted Patent US 12,332,067
Granted Patent B2
US 12,332,067 · App. 17/734,594 · Granted Jun 17, 2025

EV charging station optimization

Inventor: Jeremy Lerner (Southfield, MI)
Assignee: Ford Global Technologies, LLC
G01C21/3476G06Q10/047B60L53/00
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Quick Facts
Patent No.
US 12,332,067
App. No.
17/734,594
Granted
Jun 17, 2025
Kind
B2
Abstract

Optimizing a charger network is provided. For each vehicle trip of a plurality of vehicle trips defined by trip information maintained to a storage, a set of possible routes from an origin location of the respective vehicle trip to a destination location of the respective vehicle trip is generated using a routing algorithm. Best routes are identified based on the set of possible routes. Individual charger recommendations are identified using the best routes. Collective charger recommendations across the plurality of vehicle trips based on the individual charger recommendations. The collective charger recommendations are utilized to optimize the charger network.

Claims (47)

1. A system for optimizing a charger network, comprising:

a memory configured to store trip information, the trip information including start points and end points for routes that are traversed by vehicles and corresponding actual distance and/or time information for traversal of the routes, without storing actual routes taken from the start points to the end points by the vehicles; and

a charger locations server including a hardware processor configured to execute a charger location service to:

for each vehicle trip of a plurality of vehicle trips defined by the trip information:

generate, using a routing algorithm, a set of possible routes from an origin location of the respective vehicle trip to a destination location of the respective vehicle trip,

identify, for each start and end point pair, best routes based on the set of possible routes using the trip information to select the best routes as those closest to the corresponding actual distance and/or time information for the start and end point pair,

identify individual charger recommendations using the best routes,

identify collective charger recommendations across the plurality of vehicle trips based on the individual charger recommendations, the collective charger recommendations including which road segments of the best routes have a total count of unique vehicles that indicate additional charging stations are required to optimize the charger network, and

utilize the collective charger recommendations to optimize the charger network along the indicated road segments.

2. The system of claim 1 , wherein the trip information further defines total time of the respective vehicle trip, and the charger locations server is configured to select the best routes from the set of possible routes as those having a traversal time closest to the corresponding actual time of the respective vehicle trip for the start and end point pair.

3. The system of claim 1 , wherein the trip information further defines total distance of the respective vehicle trip, and the charger locations server is configured to select the best routes from the set of possible routes as those having a distance closest to the corresponding actual distance of the respective vehicle trip for the start and end point pair.

4. The system of claim 1 , wherein to identify a road segment for an individual charger recommendation includes determining that a closest charging station to the road segment is at least a predefined distance away from the road segment along the vehicle trip.

5. The system of claim 4 , wherein the collective charger recommendations include the road segment responsive to at least a predefined quantity of vehicle trips indicating individual charger recommendations for that road segment.

6. The system of claim 4 , wherein the collective charger recommendations include the road segment responsive to at least a predefined quantity of unique vehicles for the vehicle trips indicating individual charger recommendations for that road segment.

7. The system of claim 4 , wherein the charger locations server is further configured to:

identify a parking location in where at least a predefined minimum quantity of vehicles have parked within proximity to the road segment; and

indicate, in the collective charger recommendations, the parking location as a candidate for new charging stations along the road segment.

8. A method for system for optimizing a charger network, comprising:

for each vehicle trip of a plurality of vehicle trips defined by trip information maintained to a storage, the trip information including start points and end points for routes that are traversed by vehicles and corresponding actual distance and/or time information for traversal of the routes, without storing actual routes taken from the start points to the end points by the vehicles:

generating, using a routing algorithm, a set of possible routes from an origin location of the respective vehicle trip to a destination location of the respective vehicle trip,

identifying, for each start and end point pair, best routes based on the set of possible routes using the trip information to select the best routes as those closest to the corresponding actual distance and/or time information for the start and end point pair, and

identifying individual charger recommendations using the best routes,

identifying collective charger recommendations across the plurality of vehicle trips based on the individual charger recommendations, the collective charger recommendations including which road segments of the best routes have a total count of unique vehicles that indicate additional charging stations are required to optimize the charger network, and

utilizing the collective charger recommendations to optimize the charger network along the indicated road segments.

9. The method of claim 8 , wherein the trip information further defines total time of the respective vehicle trip, and further comprising selecting the best routes from the set of possible routes as those having a traversal time closest to the corresponding actual time of the respective vehicle trip for the start and end point pair.

10. The method of claim 8 , wherein the trip information further defines total distance of the respective vehicle trip, and further comprising selecting the best routes from the set of possible routes as those having a distance closest to the corresponding actual distance of the respective vehicle trip for the start and end point pair.

11. The method of claim 8 , wherein to identify a road segment for an individual charger recommendation includes determining that a closest charging station to the road segment is at least a predefined distance away from the road segment along the vehicle trip.

12. The method of claim 11 , wherein the collective charger recommendations include the road segment responsive to at least a predefined quantity of vehicle trips indicating individual charger recommendations for that road segment.

13. The method of claim 11 , wherein the collective charger recommendations include the road segment responsive to at least a predefined quantity of unique vehicles for the vehicle trips indicating individual charger recommendations for that road segment.

14. The method of claim 11 , further comprising:

identifying a parking location in where at least a predefined minimum quantity of vehicles have parked within proximity to the road segment; and

indicating, in the collective charger recommendations, the parking location as a candidate for new charging stations along the road segment.

15. A non-transitory computer readable medium comprising instructions for optimizing a charger network that, when executed by a hardware processor of a charger locations server, causes the charger locations server to perform operations including to:

for each vehicle trip of a plurality of vehicle trips defined by trip information maintained to a storage, the trip information including start points and end points for routes that are traversed by vehicles and corresponding actual distance and/or time information for traversal of the routes, without storing actual routes taken from the start points to the end points by the vehicles:

generate, using a routing algorithm, a set of possible routes from an origin location of the respective vehicle trip to a destination location of the respective vehicle trip,

identify, for each start and end point pair, best routes based on the set of possible routes using the trip information to select the best routes as those closest to the corresponding actual distance and/or time information for the start and end point pair, and

identify individual charger recommendations using the best routes,

identify collective charger recommendations across the plurality of vehicle trips based on the individual charger recommendations, the collective charger recommendations including which road segments of the best routes have a total count of unique vehicles that indicate additional charging stations are required to optimize the charger network, and

utilize the collective charger recommendations to optimize the charger network along the indicated road segments.

16. The medium of claim 15 , wherein the trip information further defines total time of the respective vehicle trip, and further comprising instructions that, when executed by the hardware processor, cause the charger locations server to perform operations including to select the best routes from the set of possible routes as those having a traversal time closest to the corresponding actual time of the respective vehicle trip for the start and end point pair.

17. The medium of claim 15 , wherein the trip information further defines total distance of the respective vehicle trip, and further comprising instructions that, when executed by the hardware processor, cause the charger locations server to perform operations including to select the best routes from the set of possible routes as those having a distance closest to the corresponding actual distance of the respective vehicle trip for the start and end point pair.

18. The medium of claim 15 , wherein to identify a road segment for an individual charger recommendation includes determining that a closest charging station to the road segment is at least a predefined distance away from the road segment along the vehicle trip.

19. The medium of claim 18 , wherein the collective charger recommendations include the road segment responsive to at least a predefined quantity of vehicle trips indicating individual charger recommendations for that road segment.

20. The medium of claim 18 , wherein the collective charger recommendations include the road segment responsive to at least a predefined quantity of unique vehicles for the vehicle trips indicating individual charger recommendations for that road segment.

21. The medium of claim 18 , further comprising instructions that, when executed by the hardware processor, cause the charger locations server to perform operations including to:

identify a parking location in where at least a predefined minimum quantity of vehicles have parked within proximity to the road segment; and

indicate, in the collective charger recommendations, the parking location as a candidate for new charging stations along the road segment.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 2, 2022
From: LERNER, JEREMY
To: FORD GLOBAL TECHNOLOGIES, LLC
Reel/Frame 059784/0599 →
Continuity (1)
Related Publication 20230349705A1 · Nov 2, 2023
References Cited (23)
US 9132742B2 · Dai et al. · 2015 [cited by applicant]
US 10083413B2 · Sun et al. · 2018 [cited by applicant]
US 20070294031A1 · Brinton · 2007 [cited by examiner]
US 20130317790A1 · Fukubayashi · 2013 [cited by examiner]
US 20150045985A1 · Yenamandra · 2015 [cited by examiner]
US 20170276503A1 · Oh · 2017 [cited by examiner]
US 20210107372A1 · Viswanathan et al. · 2021 [cited by applicant]
US 20230152108A1 · Vreeland · 2023 [cited by examiner]
US 20230349705A1 · Lerner · 2023 [cited by examiner]
CN 104331743A · 2015 [cited by examiner]
CN 114742340A · 2022 [cited by examiner]
CN 116049564A · 2023 [cited by examiner]
CN 116681196A · 2023 [cited by examiner]
CN-104331743-A translation (Year: 2015). [cited by examiner]
CN-116049564-A translation (Year: 2023). [cited by examiner]
Determining_Electric_Vehicle_Charging_Station_Locations_Suitability (Year: 2021). [cited by examiner]
An Electric vehicle corridor model in a dense city with applications to charging location (Year: 2021). [cited by examiner]
CN-114742340-A translation (Year: 2022). [cited by examiner]
CN-116681196-A translation (Year: 2023). [cited by examiner]
Electric_vehicle_route_planning_using_real-world_charging_infrastructure_in_Germany (Year: 2021). [cited by examiner]
Optimal_Placement_of_Electric_Vehicle_Charging_Stations_in_the_Active_Distribution_Network (Year: 2020). [cited by examiner]
System_Optimization_for_Dynamic_Wireless_Charging_Electric_Vehicles_Operating (Year: 2018). [cited by examiner]
Tsigdinos et al., Determining Electric Vehicle Charging Station Location Suitability: A Qualitative Study of Greek Stakeholders Employing Thematic Analysis and Analytical Hierarchy Process, Sustainability 2021, vol. 13,… [cited by applicant]