Method and system for operating a fleet of vehicles

Patent metadata

Field	Value
Publication number	US-12198080-B2
Application number	US-201917428166-A
Country	US
Kind code	B2
Filing date	Feb 7, 2019
Priority date	Feb 7, 2019
Publication date	Jan 14, 2025
Grant date	Jan 14, 2025

Abstract

A method for operating a plurality of vehicles is disclosed. The method comprises receiving ( 101 ) a first set of vehicle data and a second set of vehicle data, the vehicle data comprising information about each vehicle of the plurality of vehicles, each vehicle operating along at least one fixed route, receiving ( 102 ) a first set of environmental data and a second set of environmental data, the environmental data comprising information about each fixed route, and estimating ( 103 ), by means of the global self-learning model and each local-self learning model, a schedule parameter for each vehicle of the plurality of vehicles based on the received first set of vehicle data, the received first set of environmental data, the received second set of vehicle data, the received second set of environmental data, and a predefined interaction model between the global self-learning model and each local-self learning model. The method further comprises receiving ( 104 ) a measured schedule parameter for each vehicle, comparing ( 105 ) the estimated schedule parameter with the received measured schedule parameter, and updating ( 106 ) the global self-learning model and each local self-learning model based on the comparison of the estimated schedule parameter with the received measured schedule parameter.

First claim

The invention claimed is:

1. A method for operating a plurality of vehicles, each vehicle comprising an associated local self-learning model and wherein the plurality of vehicles are connected to a global self-learning model, said method comprising:
receiving a first set of vehicle data and a second set of vehicle data, the vehicle data comprising information about each vehicle of the plurality of vehicles, each vehicle operating along at least one fixed route;
receiving a first set of environmental data and a second set of environmental data, the environmental data comprising information about each fixed route;
estimating, by means of the global self-learning model and each local-self learning model, a schedule parameter for each vehicle of the plurality of vehicles based on the received first set of vehicle data, the received first set of environmental data, the received second set of vehicle data, the received second set of environmental data, and a predefined interaction model between the global self-learning model and each local-self learning model;
receiving the estimated schedule parameter for each vehicle;
receiving a measured schedule parameter for each vehicle;
comparing the estimated schedule parameter with the received measured schedule parameter;
updating the global self-learning model and each local self-learning model based on the comparison of the estimated schedule parameter with the received measured schedule parameter;
operating the plurality of vehicles based on the estimated schedule parameters; wherein the schedule parameter is a fuel consumption or a power consumption;
comparing vehicle data of a new vehicle to be added to the plurality of vehicles with the vehicle data of each vehicle of the plurality of vehicles;
selecting a new local self-learning model of the new vehicle based on the comparison and at least one predefined constraint, such that the new local self-learning model comprises a combination of one or more local self-learning models of the plurality of vehicles; and
implementing the new local self-learning model into the new vehicle based on the selection.

2. The method according to claim 1 , wherein the predefined interaction model comprises:
making a first estimation of the schedule parameter for each vehicle by means of the global self-learning model based on the received first set of vehicle data and the received first set of environmental data;
making a second estimation of the schedule parameter by means of each local self-learning model for each corresponding vehicle based on the received second set of vehicle data and the received second set of environmental data, and the first estimation of the schedule parameter for each vehicle, the second estimation being the estimated schedule parameter.

3. The method according to claim 1 , wherein the predefined interaction model comprises:
making a first estimation of the schedule parameter for each vehicle by means of the associated local self-learning model based on the received second set of vehicle data and the received second set of environmental data;
making a second estimation of the schedule parameter by means of means of the global self-learning model based on the received first set of vehicle data and the received first set of environmental data, and the first estimation of the schedule parameter for each vehicle, the second estimation being the estimated schedule parameter.

4. The method according to claim 1 , wherein the vehicle data comprises at least one of a geographical position of each vehicle, an acceleration request of each vehicle, a brake request of each vehicle, a cargo load of each vehicle, a transmission type of each vehicle, a state of charge of a traction battery of each vehicle, a state of health of the traction battery of each vehicle, and an axle load of each vehicle.

5. The method according to claim 1 , wherein the environmental data comprises at least one of weather along each fixed route, route data of each fixed route, a road curvature of each fixed route, an inclination profile of each fixed route, operational data for each fixed route, infrastructural data for each fixed route, a time of day, and calendar data.

6. The method according to claim 1 , wherein the schedule parameter is an arrival time to a destination.

7. The method according to claim 1 , wherein the vehicle data and/or the environmental data is retrieved from each vehicle of the plurality of vehicles.

8. The method according to claim 1 , wherein the vehicle data and/or the environmental data is retrieved from a data storage unit connected to the plurality of vehicles.

9. A non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a vehicle fleet management system, the one or more programs comprising instructions for performing the method according to claim 1 .

10. A system for operating a plurality of vehicles, each vehicle comprising an associated local self-learning model and wherein the plurality of vehicles are connected to a global self-learning model, the system comprising:
a first module comprising control circuitry configured to:
receive a first set of vehicle data and a second set of vehicle data, the vehicle data comprising information about each vehicle of the plurality of vehicles, each vehicle operating along at least one fixed route;
receive a first set of environmental data and a second set of environmental data, the environmental data comprising information about each fixed route;
estimate, by means of the global self-learning model and each local-self learning model, a schedule parameter for each vehicle of the plurality of vehicles based on the received first set of vehicle data, the received first set of environmental data, the received second set of vehicle data, the received second set of environmental data, and a predefined interaction model between the global self-learning model and each local-self learning model; a second module comprising a control unit to:
receive the estimated schedule parameter from the first module;
receive a measured schedule parameter for each vehicle;
compare each estimated schedule parameter with each corresponding received measured schedule parameter;
send a command signal in order to update the global self-learning model and each local self-learning model based on the comparison;
wherein the schedule parameter is a fuel consumption or a power consumption; and

a third module comprising control circuitry configured to:
compare vehicle data of a new vehicle to be added to the plurality of vehicles with the vehicle data of each vehicle of the plurality of vehicles;
select a new local self-learning model of the new vehicle based on the comparison and at least one predefined constraint, such that the new local self-learning model comprises a combination of one or more local self-learning models of the plurality of vehicles; and
implement the new local self-learning model into the new vehicle based on the selection.

11. The system according to claim 10 , wherein the predefined interaction model comprises:
making a first estimation of the schedule parameter for each vehicle by means of the global self-learning model based on the received first set of vehicle data and the received first set of environmental data;
making a second estimation of the schedule parameter by means of each local self-learning model for each corresponding vehicle based on the received second set of vehicle data and the received second set of environmental data, and the first estimation of the schedule parameter for each vehicle, the second estimation being the estimated schedule parameter.

Assignees

• Volvo Truck Corp

Inventors

• Islam Mohammad Manjurul
• Rylander David

Classifications

• G05B13/0265

  the criterion being a learning criterion · CPC title

• G06N20/00

  Machine learning · CPC title

• G06Q50/40

  Business processes related to the transportation industry (shipping G06Q10/083) · CPC title

• G06Q10/08

  Logistics, e.g. warehousing, loading or distribution; Inventory or stock management · CPC title

• G08G1/123

  indicating the position of vehicles, e.g. scheduled vehicles; {Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams}(transmission of navigation instructions to vehicles G08G1/0968) · CPC title