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

Patent metadata

Field	Value
Publication number	US-2020269719-A1
Application number	US-201916284560-A
Country	US
Kind code	A1
Filing date	Feb 25, 2019
Priority date	Feb 25, 2019
Publication date	Aug 27, 2020
Grant date	—

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 of determining a target charging level of a battery pack for a drive route, the method comprising:
receiving, by a processing device, 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, by the processing device, the data to a machine learning server; directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data; receiving, by the processing device, a vehicle route request from the vehicle, the vehicle route request corresponding to the drive route; estimating, by the processing device, travel conditions of the vehicle based on the route request; determining, by the processing device, a temperature of the battery pack in the vehicle; determining, by the processing device, a target battery charging level based on the predictive model, the travel conditions, and the temperature; and providing, by the processing device, the target battery charging level to the vehicle.

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 receiving the data pertaining to the cells within the battery pack comprises receiving at least one of cell configuration data and operational data.

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

5 . 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.

6 . The method of claim 1 , further comprising:
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.

7 . The method of claim 1 , further comprising:
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.

8 . The method of claim 7 , further comprising 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.

9 . A system configured for determining a target charging level of a battery pack for a drive route, the 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 of vehicles;
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;
receive a vehicle route request from the vehicle, the vehicle route request corresponding to the drive route;
estimate travel conditions of the vehicle based on the route request;
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
provide the target battery charging level to the vehicle.

10 . The system of claim 9 , 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.

11 . The system of claim 9 , wherein receiving the data pertaining to the cells within the battery pack comprises receiving at least one of cell configuration data and operational data.

12 . The system of claim 11 , wherein the operational data is used to determine operating conditions of the vehicle.

13 . The system of claim 9 , 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.

14 . The system of claim 9 , wherein the one or more hardware processors are further configured by 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.

15 . The system of claim 9 , wherein the one or more hardware processors are further configured by 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.

16 . The system of claim 15 , wherein the one or more hardware processors are further configured by 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.

17 . A non-transitory computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to perform a method of determining a target charging level of a battery pack for a drive route, the 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 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.

18 . The computer-readable storage medium of claim 17 , wherein the method further comprises storing the data and the predictive model in a battery database for subsequent acce

Assignees

• Toyota Res Inst Inc

Inventors

• Aykol Muratahan
• Herring Patrick K
• Anapolsky Abraham

Classifications

• G06N20/00

  Machine learning · CPC title

• Y02T90/12

  Electric charging stations · CPC title

• Y02T10/72

  Electric energy management in electromobility · CPC title

• Y02T90/14

  Plug-in electric vehicles · CPC title

• B60L50/60

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