Ford Global Technolgoies, LLC

Hyongju Park

Richard Gordon

Ben Bulat

Arnold Babila

Yan Fu

Sophia Dancel

Donald Christopher Conroy

Fiona Jane Gronowicz

Kai Wu

Shantanu Deshmukh

Don Conroy

Sarah Klein

Jacqueline Smith

Jeffrey Mclendon

Subrahmanyam Gade

Venkatarao Rebba

Dominique Meroux

An electric vehicle charging recommendation system is disclosed. The system comprises a transceiver and a processor. The transceiver is configured to receive a request from a user interface associated with a vehicle user. The request includes one or more of vehicle route information and vehicle State of Charge (SoC) level. The processor is configured to generate a geofence, and obtain real-time operator charging amounts from charging station operators located in the geofence. The processor is further configured to determine that a charging demand in the geofence is less than a predetermined threshold value, and activate a first mode when the charging demand is less than the predetermined threshold value. The processor is further configured to generate a first list of recommended charging stations based on the real-time operator charging amounts responsive to the activation of the first mode, and transmit the first list to the user interface.

Electric vehicle charging recommendation systems and methods

A cloud service receives locations of priority vehicles (PVs) and non-priority vehicles in a metropolitan area. The cloud service accesses a database storing indicia of restricted road portions in the metropolitan area. The cloud service allocates restricted road portions from the database to the NPVs based on the locations of the PVs, the locations of the NPVs, and locations of the restricted road portions. The allocating includes preventing some of the restricted road portions in the database from being available to be allocated to the NPVs based on determining, according to the locations of the PVs and NPVs and according to pre-stored routes of the PVs, that estimated uses of the road portions by the NPVs would conflict with estimated uses of the road portions by the PVs as they travel along the pre-stored routes. Routes generated to include the respectively allocated road portions are provided to the NPVs.

Algorithms for optimizing utilization

A dispatch database maintains, for a plurality of vehicles available for dispatch, vehicle data and constraints data. A processor is programmed to execute a dispatch server to perform operations including to receive a dispatch request requesting a vehicle to arrive at a request location, utilize a machine-learning model to identify one or more of the plurality of vehicles to respond to the dispatch request, the machine-learning model utilizing the vehicle data and the constraints data as inputs to determine the one or more of the plurality of vehicles, and inform the one or more of the plurality of vehicles of the dispatch request.

Vehicle dispatch using machine learning

Vehicle fuel tank modeling systems and methods are provided herein. An example method includes introducing known volumes of fuel into the fuel tank, determining correlations of fuel level percentage values relative to the known volumes of fuel placed into the fuel tank, offsetting the correlations to compensate for unusable fuel volume of the fuel tank, selecting a non-linear fuel tank model that fits the offset correlations, and applying the non-linear fuel tank model to a target vehicle having a fuel tank with a fuel tank part number.

Vehicle fuel tank modeling systems and methods

Vehicle fuel volume estimation systems and methods are provided herein. An example method includes determining a fuel level percentage value for a vehicle, determining a vehicle identification number (VIN) of the vehicle, determining a fuel tank part number using the VIN, converting the fuel level percentage value into a fuel volume value based on the fuel tank part number, and transmitting a message to a recipient, the message that includes the fuel volume value.

Vehicle fuel volume estimation systems and methods

Vehicle fuel volume consumption estimation and accuracy analysis systems and methods are provided herein. An example method includes determining a fuel level percentage estimate or a fuel volume estimate for a vehicle at a first point in time and a second point in time for a trip, the fuel level percentage estimate or the fuel volume estimate being determined using a fuel tank model of a fuel tank of a vehicle, determining connected vehicle data that includes a determination of accumulated fuel consumed during the trip, and determining an accuracy of fuel tank model using the fuel level percentage estimate or the fuel volume estimate calculated at the first point in time and the second point in time, as well as the accumulated fuel consumed during the trip.

Vehicle fuel volume consumption estimation and accuracy analysis systems and methods

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Ann Arbor, MI, United States of America

Hyongju Park