Ford Global Technolgoies, LLC

Ford Motor Company Limited

Ray C Siciak

Michael Adel Awad Alla

David Anthony Symanow

Michael E Reibling

Steven Michael Cyr

Tsung-Han Tsai

Bo Wu

Dhanunjay Vejalla

Derek Hartl

Sami Dagher

Kathryn Hamilton

Scott J Lauffer

Erik J Christen

David Crist Gabriel

Masahiro Kimoto

Prasad Venkiteswaran

Matthew Fleming

Ross Dykstra Pursifull

Ming Lang Kuang

Ralph Wayne Cunningham

David Michael Herman

Daniel A Makled

Mathew Alan Boesch

Michael Mcquillen

Ashwin Arunmozhi

David Celinske

Mark Douglas Malone

Daniel M King

Philip Michael Gonzales

Karin Lovett

Eric David Bramson

James Weinfurther

John Sturza

Frank J Desjarlais

Mahrdad Damsaz

Allen Dennis Dobryden

Yuming Niu

Vivekananda Krishnamurthy

Deborah E Callicoat

Ahmad Khalifeh

Ahmet Cinar

Vineeth Seshadri Kallur

Barnabas Joseph Nemec

Hamed Babazadehrokni

Beni Tang

Rashmi Hegde

Vincent John Boscheratto

Amy Beth Cronin

Raveen Fernando

John David Jorgensen

Robert Brown

Ramzi Ahmad Chraim

Jayeshkumar Ramani

Suvrat Jain

A computer includes a processor and a memory storing processor-executable instructions. The processor is programmed to prevent a traction battery from providing power to a vehicle powertrain below a charge threshold, and then, upon one of (a) receiving an acceleration demand above an acceleration threshold and (b) predicting that a planned maneuver classified as high acceleration will occur within a time threshold, permit the traction battery to provide power to the vehicle powertrain below the charge threshold.

Traction-battery control in hybrid powertrain

A computer includes a processor and a memory, the memory storing instructions executable by the processor to receive a request to activate a vehicle, receive diagnostic data for one or more vehicle components from an activation component, arbitrate the request and the diagnostic data to identify an activation mode of the vehicle that is one of an unpowered mode, a power-up mode, or a fully-started mode, and instruct the activation component to activate one or more vehicle components associated with the identified activation mode.

Enhanced vehicle activation

Techniques and examples pertaining to baggage transportation in a vehicle-sharing environment are described. An automotive vehicle may have a plurality of baggage compartments each respectively having a size capacity and a weight capacity. The vehicle may also have a plurality of sensors capable of monitoring a loading situation of the plurality of baggage compartments. The vehicle may further have a memory element capable of storing capacity data representing the loading situation, as well as a processor capable of updating the capacity data in an event that the loading situation is changed due to a baggage having been loaded to, or unloaded from, a baggage compartment of the plurality of the baggage compartment. The automotive vehicle may be an autonomous ride-sharing vehicle.

Baggage transportation using autonomous vehicle

A system includes a processor and a memory. The memory stores instructions executable by the processor to determine an action for a vehicle based on an identified risk condition, to determine a minimum time for performing the action, and based on data including a vehicle battery state of charge, an outdoor temperature, and a vehicle mode of operation, to determine an available electrical power for performing the action. The instructions further include instructions to determine maximum permissible electrical energy discharge rate based on the minimum time for performing the action and the determined available electrical power, to determine a load control plan for a plurality of electrical devices in the vehicle based on the maximum permissible electrical energy discharge rate, an electrical load status of the vehicle, and load priority data; and to execute the determined load control plan.

Vehicle load shedding

A plurality of fault conditions are detected on a communication network onboard a vehicle. The detected fault conditions, a fault condition importance, environment conditions, and a vehicle operation mode are input to a neural network that outputs rankings for respective detected fault conditions. The neural network is trained by determining a loss function based on a maximum likelihood principle that determines a probability distribution that ranks the detected fault conditions. The vehicle is operated based on the rankings of the fault conditions.

Ranking fault conditions

A computer in a vehicle is programmed to receive sensor data about a vehicle occupant, select a category of behavior of the occupant based on the sensor data, and navigate to a predetermined location based on the selected category. The vehicle may be an autonomous vehicle that can be operated by the computer.

Autonomous vehicle computer

A computer includes a processor and a memory, the memory storing instructions executable by the processor to predict a user arrival time based on data of a user and a user origin location, deactivate a propulsion of the vehicle upon arriving at the user origin location, and deactivate one or more additional vehicle components based on the user arrival time.

Enhanced vehicle operation

A computer includes a processor and a memory, the memory storing instructions executable by the processor to collect steering, speed, and position data about a plurality of vehicles from one or more infrastructure sensors, identify a vehicle that varies from a specified position in a roadway lane relative to a roadway lane marker or exceeds a threshold speed based on the collected data, instruct the identified vehicle to move to a side of a roadway, and send a message to a central server including an identification of the vehicle.

A method including determining a status of a power supply of a first telematics control unit (TCU) as one of active or inactive, the first TCU programmed to communicate with a server via a network, determining a status of a second TCU in communication with the first TCU via a vehicle network, the second TCU is programmed to communicate with the server via the network, and determining a risk condition based on the statuses of the power supply and the second TCU. The risk condition is a measure of severity of an inactive status.

Vehicle telematics system

A vehicle power network includes a bus providing power to an automotive safety integrity level (ASIL)-rated load and an auxiliary-rated load. The ASIL-rated load includes a processor and memory storing instructions executable by the processor. The instructions include, to, in response to determining a minimal risk condition (MRC), selectively terminate power to the auxiliary-rated load.

Hybrid power network for a vehicle

A system includes a first power net in a vehicle electrically coupled to a first control unit. The first control unit controls first lights included in a second light set when the first power net is operational. A second power net in the vehicle is electrically coupled to the second light set and a second control unit. The second control unit controls the first lights when the first power net includes a fault condition.

Power distribution system for vehicle lighting

A vehicle charging station according to an exemplary aspect of the present disclosure includes, among other things, a cooling system configured to communicate a cooling airflow to a portion of a thermal management system located onboard an electrified vehicle, the cooling system including a fan and a chiller assembly.

Charging station for electrified vehicles

This disclosure is generally directed to systems and methods for optimizing a travel route of a hybrid-electric vehicle inside an emissions-free zone. In one exemplary embodiment, a travel route optimization system can be used to determine a charge level of a battery in the hybrid-electric vehicle and to locate charging stations inside and/or outside the emissions-free zone. The charge level provides an indication of a distance that the hybrid-electric vehicle can travel before a gasoline engine is started. The system can use factors such as the location of the charging stations and the charge level of the battery to generate an optimized travel route for the hybrid-electric vehicle that eliminates starting of the gasoline engine inside the emissions-free zone. The optimized travel route may be updated in real-time so as to allow recharging of the battery on an as-needed basis at a charging station inside or outside the emissions-free zone.

Systems and methods for optimizing a travel route of a hybrid-electric vehicle inside an emissions-free zone

A system, comprising a computer that includes a processor and a memory, the memory storing instructions executable by the processor to receive authentication an operator of a service controller from a server computer, identify a vehicle by scanning a physical code with the service controller and pair the service controller with the vehicle. The vehicle can be operated with the service controller.

Vehicle service controller

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Ann Arbor, MI, United States of America

Ray C Siciak