Systems, methods, and storage media for determining a target battery charging level for a drive route

Patent metadata

Field	Value
Publication number	US-11325494-B2
Application number	US-201916284560-A
Country	US
Kind code	B2
Filing date	Feb 25, 2019
Priority date	Feb 25, 2019
Publication date	May 10, 2022
Grant date	May 10, 2022

Abstract

Systems, methods, and storage media for determining a target charging level of a battery pack for a drive route are disclosed. A method includes receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet of vehicles, providing the data to a machine learning server, directing the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, receiving a vehicle route request from the vehicle, the vehicle route request corresponding to the drive route, estimating travel conditions of the vehicle based on the route request, determining a temperature of the battery pack in the vehicle, determining a target battery charging level based on the predictive model, the travel conditions, and the temperature, and providing the target battery charging level to the vehicle.

First claim

What is claimed is:

1. A method, comprising:
receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet and comprising at least one of cell configuration data indicating an arrangement of cells within the battery pack and operational data for each cell;
providing the data to a machine learning server;
directing the machine learning server to generate a predictive model based on machine learning of the data;
estimating travel conditions of the vehicle based on a received route request corresponding to a drive route;
determining a temperature of the battery pack in the vehicle;
determining a target battery charging level based on the predictive model, the travel conditions, and the temperature; and
causing the battery pack to be charged according to the target battery charging level.

2. The method of claim 1 , further comprising storing the data and the predictive model in a battery database for subsequent access by the machine learning server.

3. The method of claim 1 , wherein the operational data is used to determine operating conditions of the vehicle.

4. The method of claim 1 , wherein estimating the travel conditions of the vehicle based on the route request comprises one or more of:
receiving supplemental data from one or more vehicle-specific sensors in the vehicle;
determining a distance for each of a plurality of routes;
determining a type of terrain traversed on each of the plurality of routes;
determining other factors that affect travel on each of the plurality of routes; and
determining one or more environmental conditions that affect travel and battery pack discharge.

5. The method of claim 1 , wherein causing the battery pack to be charged further comprises:
determining that a current battery charging level of the battery pack is equal to or greater than the target battery charging level; and
directing the vehicle to terminate additional charging of the battery pack.

6. The method of claim 1 , wherein causing the battery pack to be charged further comprises:
determining that a current battery charging level of the battery pack is less than the target battery charging level; and
providing one or more instructions to charge the battery pack to the target battery charging level.

7. The method of claim 6 , wherein causing the battery pack to be charged further comprises directing the vehicle to cease charging the battery pack once the current battery charging level of the battery pack has reached the target battery charging level.

8. A system, comprising:
a fleet of vehicles, each vehicle in the fleet of vehicles comprising a battery pack having a plurality of cells; and
one or more hardware processors communicatively coupled to each vehicle in the fleet of vehicles and to the one or more battery testing devices, the one or more hardware processors configured by machine-readable instructions to:
receive data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet and comprising at least one of cell configuration data indicating an arrangement of cells within the battery pack and operational data for each cell;
provide the data to a machine learning server;
direct the machine learning server to generate a predictive model, the predictive model based on machine learning of the data;
estimate travel conditions of the vehicle based on a received route request corresponding to a drive route;
determine a temperature of the battery pack in the vehicle;
determine a target battery charging level based on the predictive model, the travel conditions, and the temperature; and
cause the battery pack to be charged according to the target battery charging level.

9. The system of claim 8 , further comprising a battery database communicatively coupled to the one or more hardware processors, wherein the one or more hardware processors are further configured by machine-readable instructions to store the data and the predictive model in the battery database.

10. The system of claim 8 , wherein the operational data is used to determine operating conditions of the vehicle.

11. The system of claim 8 , wherein estimating the travel conditions of the vehicle based on the route request comprises one or more of:
receiving supplemental data from one or more vehicle-specific sensors in the vehicle;
determining a distance for each of a plurality of routes;
determining a type of terrain traversed on each of the plurality of routes;
determining other factors that affect travel on each of the plurality of routes; and
determining one or more environmental conditions that affect travel and battery pack discharge.

12. The system of claim 8 , wherein the one or more hardware processors are further configured by the machine-readable instructions to:
determine that a current battery charging level of the battery pack is equal to or greater than the target battery charging level; and
direct the vehicle to terminate additional charging of the battery pack.

13. The system of claim 8 , wherein the one or more hardware processors are further configured by the machine-readable instructions to:
determine that a current battery charging level of the battery pack is less than the target battery charging level; and
provide one or more instructions to charge the battery pack to the target battery charging level.

14. The system of claim 13 , wherein the one or more hardware processors are further configured by the machine-readable instructions to direct the vehicle to cease charging the battery pack once the current battery charging level of the battery pack has reached the target battery charging level.

15. A non-transitory computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to perform a method comprising:
receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet and comprising at least one of cell configuration data indicating an arrangement of cells within the battery pack and operational data for each cell;
providing the data to a machine learning server;
directing the machine learning server to generate a predictive model, the predictive model based on machine learning of the data;
estimating travel conditions of the vehicle based on a received route request corresponding to a drive route;
determining a temperature of the battery pack in the vehicle;
determining a target battery charging level based on the predictive model, the travel conditions, and the temperature; and
causing the battery pack to be charged according to the target battery charging level.

16. The non-transitory computer-readable storage medium of claim 15 , wherein the method further comprises storing the data and the predictive model in a battery database for subsequent access by the machine learning server.

17. The non-transitory computer-readable storage medium of claim 15 , wherein causing the battery pack to be charged further comprises:
determining that a current battery charging level of the battery pack is equal to or greater than the target battery charging level; and
directing the vehicle to terminate additional charging of the battery pack.

18. The non-transitory computer-readable storage medium of claim 15 , wherein causing the battery pack to be c

Assignees

• Toyota Res Inst Inc

Inventors

• Aykol Muratahan
• Herring Patrick K
• Anapolsky Abraham

Classifications

• B60L58/10

  for monitoring or controlling batteries · CPC title

• B60L2240/64

  Road conditions · CPC title

• B60L50/60

  using power supplied by batteries (in combination with fuel cells B60L50/75) · CPC title

• Y02T90/12

  Electric charging stations · CPC title

• B60L2250/12

  by confirmation, e.g. of the input · CPC title