Gm Global Technolgoy Operations, Inc.

Daniel Urieli

Klaus Trangbaek

Vladimir Suplin

Ron M Hecht

Ariel Telpaz

Nadav Baron

Barak Hershkovitz

Ravid Erez

Refael Blanca

Jeremie Benichou

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 predicting energy consumption by a vehicle during a trip has a controller adapted to receive a route plan for the trip. The controller has a processor and tangible, non-transitory memory on which instructions are recorded. The system has a plurality of machine learning modules, including a behavior predictor, a driving consumption predictor and an auxiliary consumption predictor. The controller is adapted to predict a total energy consumed by the vehicle during the trip by executing the plurality of machine learning modules. The driving consumption predictor is adapted to predict a primary energy consumed for propelling the vehicle based in part on the route plan. The auxiliary consumption predictor is adapted to predict a secondary energy consumed by the vehicle for non-propulsion purposes. The behavior predictor is adapted to predict an average vehicle speed based at least partially on traffic conditions.

Modular machine-learning based system for predicting vehicle energy consumption during a trip

The concepts described herein relate to methods, systems, and analytical techniques to achieve a cloud-based electrified vehicle energy usage system that includes a cloud-based server that is operative to receive vehicle-specific electric energy-related data from a plurality of connected electrified vehicles, and correlate the vehicle-specific electric energy-related data to a road segment. A data analysis is executed for the vehicle-specific electric energy-related data for the plurality of connected electrified vehicles for the road segment, and a crowd-sourced electrified vehicle energy usage report is generated for the road segment based upon the data analysis.

Method and system for cloud-based electrified vehicle energy usage system

A system for adaptive in-drive updating, for a vehicle travelling on a route, includes a controller having a processor and tangible, non-transitory memory. The vehicle is carrying a load. The controller is adapted to obtain one or more dynamic parameters pertaining to the load. A plurality of adaptive predictors is selectively executable by the controller at a timepoint during the route at which a completed portion of the route has been traversed by the vehicle and a remaining portion remains untraversed. The plurality of adaptive predictors includes a speed predictor configured to generate a global speed profile. The plurality of adaptive predictors includes a driving consumption predictor is configured to predict a driving consumption profile for the remaining portion of the route based in part on the dynamic parameter, the route features, the global speed profile, and a past drive consumption.

Adaptive in-drive updating of energy consumption prediction for vehicle with a load

A system for adaptive in-drive updating, for a vehicle travelling on a route, includes a controller adapted to obtain a pre-drive energy consumption prediction for the route, via an energy consumption predictor. An in-drive updating module is selectively executable by the controller at a timepoint during the route at which a completed portion of the route has been traversed and a remaining portion remains untraversed. The controller is adapted to obtain an actual energy consumption for segments in the completed portion of the route. The controller is adapted to obtain at least one modification factor based on a comparison of the actual energy consumption and the pre-drive energy consumption prediction for the segments in the completed portion of the route. The pre-drive energy consumption prediction for the remaining portion of the route is adjusted based on the modification factor.

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Herzliya, Israel

Daniel Urieli