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1. WO2020160778 - PROCÉDÉ ET SYSTÈME PERMETTANT DE FAIRE FONCTIONNER UNE PLURALITÉ DE VÉHICULES

Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

[ EN ]

CLAIMS

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 (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;

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; 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.

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 any one of the preceding claims, further comprising: comparing vehicle data of the new vehicle with the vehicle data of each vehicle of the plurality of vehicles;

selecting a local-self learning model of one of the vehicles of the plurality of based on the comparison and at least one predefined constraint; and

implementing the selected local self-learning model into a new vehicle to be added to the plurality of vehicles.

5. The method according to any one of the preceding claims, 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.

6. The method according to any one of the preceding claims, 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.

7. The method according to any one of the preceding claims, wherein the schedule parameter is an arrival time to a destination, a fuel consumption, or a power consumption.

8. The method according to any one of the preceding claims, wherein the vehicle data and/or the environmental data is retrieved from each vehicle of the plurality of vehicles.

9. The method according to any one of the preceding claims, wherein the vehicle data and/or the environmental data is retrieved from a data storage unit connected to the plurality of vehicles.

10. 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 any one of claims 1 - 9.

11. 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.

12. The system according to claim 11, 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.

13. The system according to claim 11, 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.

14. The system according to claim 12 or 13, further comprising a third module comprising a control circuitry configured to:

compare vehicle data of a new vehicle to be added to the fleet of vehicles with the vehicle data of each vehicle of the plurality of vehicles;

selecting a local self-learning model of at least one vehicle of the plurality of vehicles based on the comparison and at least one predefined constraint; and

implement a local self-learning model into the new vehicle based on the selection.