Ford Global Technolgoies, LLC

Swati Johar Rawat

Fling Finn Tseng

Himanshu Verma

Kalyani Purushottam Sonawane

Kwaku O Prakah-Asante

Johannes Geir Kristinsson

Bhagyashri Satyabodha Katti

Dimitar Petrov Filev

Patrick Maloney

Shiqi Qiu

Alex Perkins

Nikhil Kailas Jamdade

Electric vehicle battery monitoring systems and methods are disclosed herein. An example method includes displaying a state of charge for a battery of an electric vehicle on a display screen of the electric vehicle, determining a frequency of how often a user views the state of charge, inferring that the user has anxiety over a range for the electric vehicle based on the frequency, and presenting the user with options to extend the state of charge.

Electric vehicle battery monitoring systems and methods

A vehicle charging management method is disclosed. The method may include obtaining a first input associated with a vehicle. The first input may include a state of charge (SOC) level of a vehicle battery, a distance travelled by the vehicle since last charge, and time since the last charge. The method may further include obtaining a second input associated with historical vehicle battery charging information. Responsive to obtaining the first input and the second input, the method may include determining a vehicle user intent to charge the vehicle battery. The method may further include comparing the vehicle user intent with a threshold value, and transmitting a notification to a communication device when the vehicle user intent is greater than the threshold value.

Vehicle charge intent prediction and recommendation systems and methods

Systems and methods for personalized route prediction including receiving, at a first time, first input data associated with a first route; populating a first database with the input data; receiving, at a third time, second input data associated with a second route; comparing the second input data to the first input data included within the first database; determining, based on the comparison, a first cluster including the first data and the second input data or a second cluster including the second input data; populating a second database based on the first cluster or the second cluster; determining, using the first database and at a second time, at least one of: predicted departure data, predicted destination data, and/or predicted route data; and causing, based on the predicted departure data, predicted destination data, and/or predicted route data, to perform an action in association with a vehicle.

Systems and methods for personalized route prediction

A profile for an occupant is stored. The profile includes a plurality of clusters of sitting positions for the occupant in a seat and classifications of respective clusters as preferred or nonpreferred. Respective clusters are classified as preferred or nonpreferred based on sitting scores for the respective sitting positions in that cluster. A higher sitting score increases a likelihood of the classification being preferred. A series of pressure maps indicating a respective series of sitting positions of the occupant in the seat are received. The pressure maps include a current pressure map. One of the sitting positions is assigned to one of the clusters that is classified as preferred in response to the sitting score of that sitting position being greater than a threshold score. A physical configuration of the seat is adjusted in response to the current sitting position being in one of the clusters that is classified as nonpreferred.

Seat adjustment

The disclosure generally pertains to recommending an alternative off-road track to a driver of a vehicle. In an example method, a processor receives a sensor signal that is generated in response to an operating condition of the vehicle on an off-road track. The operating condition can include, for example, an angular orientation of a chassis portion of the vehicle with respect to a ground surface (yaw, pitch, roll, etc.), steering characteristics, speed characteristics, and/or braking characteristics. The processor evaluates the sensor signal to identify an attribute of the off-road track, such as, for example, a difficulty factor and/or a challenge level. The processor may assign a track score to the off-road track based on the attribute and may provide to the driver, a recommendation for use of another off-road track based on comparing the first track score to a second track score associated with the other off-road track.

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Mississauga, Canada

Swati Johar Rawat