公开(公告)号：US20250029026A1

公开(公告)日：2025-01-23

申请号：US18355014

申请日：2023-07-19

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Ariel Telpaz ,  Zahy Bnaya ,  Refael Blanca ,  Nadav Baron ,  Neeraj R. Gautama ,  Laura Chmielewski

IPC: G06Q10/0631 ,  G06Q10/04 ,  G06Q30/0283 ,  G06Q30/08

Abstract: A system and method for resource sharing among vehicle fleets using a bidding mechanism is presented. A fleet scheduler generates a bidding proposal for one or more unassigned tasks associated with a first fleet of vehicles, where the bidding proposal is associated with a second fleet of vehicles associated with an excess quantity of resources. An online platform receives the bidding proposal for the one or more unassigned tasks and determines, using the bidding proposal and based on an auctioning scheme, one or more winning bids. The one or more winning bids includes assigning the one or more unassigned tasks from the first fleet of vehicles to the second fleet of vehicles with the excess quantity of resources.

公开(公告)号：US20230305568A1

公开(公告)日：2023-09-28

申请号：US17703066

申请日：2022-03-24

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Ariel Telpaz ,  Refael Blanca ,  Nadav Baron ,  Ravid Erez ,  Daniel Urieli ,  Ron Hecht ,  Barak Hershkovitz

IPC: G05D1/02 ,  B60L58/12

CPC classification number: G05D1/0217 ,  G05D1/0291 ,  G05D1/0225 ,  B60L58/12

Abstract: A system for managing a fleet of electric vehicles and respective fleet drivers includes a command unit having a processor and tangible, non-transitory memory on which instructions are recorded. The command unit is adapted to obtain input variables, including respective fleet tasks and their priority status. Route data for the respective fleet tasks is obtained. The command unit is adapted to obtain an objective function defined by a plurality of influence factors having respective weights. The command unit is adapted to obtain optimal charging schedules respectively for the electric vehicles and match the respective fleet tasks to the electric vehicles and the respective fleet drivers, based in part on the objective function, input variables and the route data. The influence factors may include energy cost optimization, timeliness of task completion and minimizing range anxiety. In some embodiments, the respective weights of the influence factors are designated by a fleet manager.

公开(公告)号：US20240263957A1

公开(公告)日：2024-08-08

申请号：US18098956

申请日：2023-01-19

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Ariel Telpaz ,  Refael Blanca ,  Zahy Bnaya ,  Laura Chmielewski ,  Michelle L. Calloway ,  Neeraj R. Gautama

IPC: G01C21/36 ,  G01C21/34

CPC classification number: G01C21/3617 ,  G01C21/3469 ,  G01C21/3492

Abstract: A system and method for determining driver assignments based on fleet trips history logs is presented. The system and method include receiving, at a server or online platform, a set of fleet driver requirements from a fleet owner. The server receives fleet trip data from a vehicle with an integrated communication device where a weighted trip score for a driver based on a particular fleet owner is determined. The server also ranks one or more drivers, based on the weighted trip score, that best matches the fleet driver requirements. The weighted trip score is determined based on the fleet trip data received from the integrated communication device in the vehicle where the fleet trip data includes at least a start trip information, a trip log, an event trigger, and an end of trip data.

公开(公告)号：US20240013105A1

公开(公告)日：2024-01-11

申请号：US17857496

申请日：2022-07-05

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Zahy Bnaya ,  Nadav Baron ,  Refael Blanca ,  Ravid Erez ,  Ariel Telpaz

IPC: G06Q10/04 ,  G08G1/00

CPC classification number: G06Q10/047 ,  G08G1/20

Abstract: A system for optimizing ownership cost of a fleet having electric vehicles includes a command unit adapted to selectively execute a simulation module, a sampling module and an optimization module. The command unit is configured to construct a multi-agent model based at least partially on historical fleet trip data and mobility pattern data of the fleet. Route data for a set of fleet tasks is obtained, including charging infrastructure data. The command unit is configured to simulate different configurations of the electric vehicles carrying out the set of fleet tasks over a predefined period, via the simulation module, based in part on the multi-agent model and the route data. The command unit is configured to determine an optimal configuration from the different configurations of the electric vehicles, via the optimization module. The optimal configuration minimizes investment and operational costs of the fleet.