Gm Global Technolgoy Operations, Inc.

Refael Blanca

Ariel Telpaz

Zahy Bnaya

Nadav Baron

Neeraj Roy Gautama

Laura R Chmielewski

Ravid Erez

Michelle L Calloway

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.

Resource sharing among vehicle fleets using bidding mechanism

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.

Fleet telemetry data sourcing and evaluation tool

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.

Ownership cost optimization for fleet with electric vehicles

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West Bloomfield Township, MI, United States of America

Refael Blanca