Gm Global Technolgoy Operations, Inc.

Nadav Baron

Ariel Telpaz

Barak Hershkovitz

Ravid Erez

Yuval Gilboa

Zahy Bnaya

Refael Blanca

Ron M Hecht

Neeraj Roy Gautama

Michael Baltaxe

Todd P Lindemann

Laura R Chmielewski

Michael R Colville

Daniel Urieli

Christopher A Stanek

Richard B Weinberg

Benjamin Perahya

Oleg Karp

Omer Zerbib

Boris Kabisher

Dima Zevelev

Shani Avnet

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 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.

System and method for managing fleet of electric vehicles

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

Presented are intelligent vehicle systems for off-road driving incident prediction and assistance, methods for making/operating such systems, and vehicles networking with such systems. A method for operating a motor vehicle includes a system controller receiving geolocation data indicating the vehicle is in or entering off-road terrain. Responsive to the vehicle geolocation data, the controller receives, from vehicle-mounted cameras, camera-generated images each containing the vehicle's drive wheel(s) and/or the off-road terrain's surface. The controller receives, from a controller area network bus, vehicle operating characteristics data and vehicle dynamics data for the motor vehicle. The camera data, vehicle operating characteristics data, and vehicle dynamics data is processed via a convolutional neural network backbone to predict occurrence of a driving incident on the off-road terrain within a prediction time horizon. The system controller commands a resident vehicle system to execute a control operation responsive to the predicted occurrence of the driving incident.

Intelligent vehicle systems and control logic for incident prediction and assistance in off-road driving situations

A computer-implemented method for scheduling pre-departure charging for electric vehicles includes predicting a user-departure time based on a first machine learning prediction model. The method further includes determining a cabin temperature to be set for the user at the user-departure time based on a second machine learning prediction model. The method further includes determining a battery-temperature to be set at the user-departure time based on a third machine learning prediction model. The method further includes determining a present charge level of a battery of the electric vehicle. The method further includes computing a charging start-time to start charging the battery based on one or more attributes of a charging station to which the electric vehicle is coupled, and based on the user-departure time, the cabin temperature, and the battery-temperature. The method further includes initiating charging the battery at the charging start-time.

Scheduling pre-departure charging of electric vehicles

An operating system for a vehicle having an electric vehicle (EV) drivetrain and a plurality of electrically-powered accessories is described. A controller determines, via a navigation system, a target off-road trail segment, and characterizes the subject vehicle, ambient conditions, and the target off-road trail segment to determine an estimated consumption of electric energy for the vehicle to operate over the target off-road trail segment. The EV drivetrain and the electrically-powered accessories are controlled during operation of the vehicle on the off-road trail segment based upon the estimated consumption of electric energy for the subject vehicle. This is done to minimize a likelihood of a low SOC event for the DC power source for the trail segment and to avoid a low battery state at a location that is distal from a charging station.

Method and system for operating an electric vehicle in off-road conditions

A system and method of tracking energy usage in a vehicle. The method, in one implementation, involves building an energy usage prediction model, obtaining a planned route for a vehicle that contains one or more planned route segments, applying the energy usage prediction model to each planned route segment of the one or more planned route segments of the planned route to obtain an energy usage plan, receiving onboard data from the vehicle, constructing an actual route based on the onboard data, and performing a match analysis between the planned route and the actual route based on the onboard data.

Vehicle energy usage tracking

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Herzliya, Israel

Nadav Baron