Systems, methods, and storage media for predicting a discharge profile of a battery pack

Patent metadata

Field	Value
Publication number	US-2020271725-A1
Application number	US-201916284533-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 generating a predicted discharge profile of a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles operating under a plurality of conditions, 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, generating, by the processing device, the predicted discharge profile of the vehicle battery pack from the predictive model, and providing the discharge profile to an external device.

First claim

What is claimed is:

1 . A method for generating a predicted discharge profile of a vehicle battery pack, 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 operating under a plurality of conditions, 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; generating, by the processing device, the predicted discharge profile of the vehicle battery pack from the predictive model; and providing the discharge profile to an external device.

2 . The method of claim 1 , further comprising storing the data, the predictive model, and the discharge profile in a battery database.

3 . The method of claim 1 , further comprising generating, by the processing device, one or more subspaces in the discharge profile.

4 . 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.

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

6 . The method of claim 1 , wherein receiving the data comprises receiving supplemental data from one or more vehicle-specific sensors in each vehicle of the fleet of vehicles.

7 . The method of claim 1 , further comprising receiving diagnostic cycling data from one or more battery testing devices configured to simulate discharge conditions of one or more additional battery packs.

8 . The method of claim 1 , wherein the data pertaining to the cells within the battery pack indicates how the battery pack discharges when fully charged versus how the battery pack discharges when less than fully charged.

9 . A system configured for generating a predicted discharge profile of a vehicle battery pack, 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, 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 operating under a plurality of conditions, 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;
generate the predicted discharge profile of the vehicle battery pack from the predictive model; and
provide the discharge profile to an external device.

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, the predictive model, and the discharge profile in the battery database.

11 . The system of claim 9 , wherein the one or more hardware processors are further configured by machine-readable instructions to generate one or more subspaces in the discharge profile.

12 . 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.

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

14 . The system of claim 9 , wherein receiving the data comprises receiving supplemental data from one or more vehicle-specific sensors in each vehicle of the fleet of vehicles.

15 . The system of claim 9 , further comprising one or more battery testing devices communicatively coupled to the one or more hardware processors, the one or more battery testing devices configured to simulate discharge conditions of one or more additional battery packs.

16 . The system of claim 15 , wherein the one or more hardware processors are further configured by machine-readable instructions to receive diagnostic cycling data from the one or more battery testing devices.

17 . The system of claim 15 , wherein the one or more battery testing devices comprise one or more high-throughput (HT) cyclers.

18 . A non-transitory computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to perform a method for generating a predicted discharge profile of a vehicle battery pack, the method comprising:
receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles operating under a plurality of conditions, 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; generating the predicted discharge profile of the vehicle battery pack from the predictive model; and providing the discharge profile to an external device.

19 . The computer-readable storage medium of claim 17 , wherein the method further comprises generating one or more subspaces in the discharge profile.

20 . The computer-readable storage medium of claim 17 , wherein the method further comprises receiving diagnostic cycling data from one or more battery testing devices configured to simulate discharge conditions of one or more additional battery packs.

Assignees

• Toyota Res Inst Inc

Inventors

• Herring Patrick K
• Aykol Muratahan
• Anapolsky Abraham

Classifications

• Y02T10/72

  Electric energy management in electromobility · CPC title

• Y02T90/16

  Information or communication technologies improving the operation of electric vehicles · CPC title

• Y02T10/70

  Energy storage systems for electromobility, e.g. batteries · CPC title

• H04L67/12

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title

• G01R31/367 Primary

  Software therefor, e.g. for battery testing using modelling or look-up tables · CPC title