Gm Global Technolgoy Operations, Inc.

Todd P Lindemann

Richard B Weinberg

Patrick Frost

Christopher A Kinser

Patricia M Laskowsky

Rory B Fraga

Rachel A White

Paul G Otanez

Andrew M Zettel

Allan K Lewis

Yiran Hu

Yuval Gilboa

Adam J Heisel

Nicholas John Kalweit

Chia-Hsiang Liu

Matthew Allan Herrmann

Nadav Baron

Apral S Hara

James B Nicholson

Jacob M Knueven

Nathaniel S Michaluk

Barak Hershkovitz

Kevin J Smith

Eugenie T Abboud

Freddy V Rayes

Teddy Siy

Kunaal Verma

Danielle A Cory

Terence L Meehan

Jason P Hafer

Antonio R Vittorini

David S Park

Mark D Miller

Dima Zevelev

Ramon A Alonso

Bryce J Benfield

Vukasin Denic

Eric F Gorski

John M Clarahan

Georges H Tamer

Robert Stacey

A method of operating a vehicle includes a vehicle controller receiving a driver acceleration/deceleration command for the vehicle's powertrain and determining a torque request corresponding to the driver's acceleration command. The controller shapes the torque request and determines compensated and uncompensated accelerations from the shaped torque request. The compensated acceleration is based on an estimated road grade and an estimated vehicle mass, whereas the uncompensated acceleration is based on a zero road grade and a nominal vehicle mass. A final speed horizon profile is calculated as: a speed-control speed profile based on the uncompensated acceleration if the vehicle's speed is below a preset low vehicle speed; or a torque-control speed profile based on a blend of the compensated and uncompensated accelerations if the vehicle speed exceeds the preset low vehicle speed. The controller commands the powertrain to output a requested axle torque based on the final speed horizon profile.

Intelligent motor vehicles and control logic for speed horizon generation and transition for one-pedal driving

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

Presented are intelligent vehicle systems and control logic for driver coaching and on-demand vehicle charging, methods for making/using such systems, and motor vehicles with real-time eco-routing and automated driving capabilities. A method for controlling operation of a vehicle includes: determining an origin and destination for the vehicle; conducting a geospatial query to identify a candidate route for traversing from the origin to the destination; determining, based on current electrical characteristics of the vehicle's battery pack, an estimated driving range for the vehicle; responsive to the estimated driving range being less than the candidate route's distance, evaluating energy characteristics of the candidate route to derive an estimated energy expenditure to reach the destination; using the estimated energy expenditure, generating an action plan with vehicle maneuvering and/or accessory usage actions that extend the estimated driving range; and commanding a resident vehicle subsystem to execute a control operation based on the action plan.

Intelligent motor vehicles, systems, and control logic for driver behavior coaching and on-demand mobile charging

A vehicle includes drive wheels, an energy source having an available energy, a torque-generating device powered by the energy source to provide an input torque, a transmission configured to receive the input torque and deliver an output torque to the set of drive wheels, and a controller. The controller, as part of a programmed method, predicts consumption of the available energy along a predetermined travel route using onboard data, offboard data, and a first logic block, and also corrects the predicted energy consumption using the onboard data, offboard data, and an error correction loop between a second logic block and the first logic block. The controller also executes a control action with respect to the vehicle using the corrected energy consumption, including changing a logic state of the vehicle.

Vehicle with model-based route energy prediction, correction, and optimization

A system for optimizing life of a battery pack in a plug-in vehicle includes sensors for measuring battery performance data, including an open-circuit voltage, charging current, and/or temperature of the battery pack, a GPS receiver, a user interface, and a controller. The controller executes a method to monitor degradation of the battery pack using the performance data, and determines driving and charging histories for an operator of the vehicle using the measured battery performance data and a position signal from the GPS receiver. The histories identify the days, hours, and locations at which the operator has driven the vehicle and charged the battery pack. The controller identifies a state of charge data bin missing performance data or containing old performance data. The controller then controls a charging operation of the battery pack via a charging control signal, and records the measured battery performance data for the identified SOC data bin.

Adaptive system and method for optimizing battery life in a plug-in vehicle

A system and method for optimizing a plug-in vehicle fleet having a plurality of battery packs across the vehicle fleet. Each vehicle includes a battery pack. The system includes sensors for measuring battery performance data and includes an open-circuit voltage, charging current, and/or temperature of the battery pack, a GPS receiver, a user interface, and a controller. The controller executes a method to monitor degradation of each battery pack using a variety of current and historical data points. The controller then controls a charging operation of each battery pack in a fleet via a charging control signal sent to a particular vehicle in the fleet. The controller also then determines the new location for each particular vehicle in a fleet.

Adaptive system and method for optimizing a fleet of plug-in vehicles

A system to display an image on a vehicle window including a memory, controller, transceiver, external display, sensor, and a plurality of windows. The memory includes one or more executable instructions. The controller is configured to execute the instructions. The transceiver can receive data transmissions. The external display is configured to exhibit an image on the windows, the exhibited image being visible in the external vehicle environment. The sensor measures a selected target being in the vehicle environment. The sensor signally communicates target measurement. The instructions enable the controller to: receive the data transmission with digital image information; operate the sensor to make at least one target measurement of the selected target; receive the target measurement signal; determine the location of the selected target; determine the window to be closest to the selected target; and operate the external display to exhibit a representative image.

Vehicle window image display system and method

Methods and systems for determining deviations is expected range, expected fuel economy, or both, for a vehicle. In accordance with one embodiment, a system includes a sensor unit and a processor. The sensor unit is configured to at least facilitate obtaining inputs for one or more factors having an effect on energy performance and/or fuel efficiency for a vehicle. The processor is coupled to the sensor unit, and is configured to at least facilitate determining a change in expected range for the vehicle from the one or more factors based on the inputs.

Determination of deviation of vehicle range or fuel economy

A system for use with a battery and sensors operable for measuring battery data includes an interactive user interface and a controller programmed with battery degradation monitoring logic for estimating a state of the battery. As part of a method, the controller identifies data bins for which measured state of charge range-based battery performance data is missing or dated/stale, and automatically prompts an operator to execute an assigned task corresponding to the identified data bins. The controller records the battery performance data for the identified data bins upon completion of the assigned task, estimates the state of the battery using the recorded battery performance data and the battery degradation monitoring logic, and executes a control action with respect to the system using the estimated state. A virtual or actual reward feature may be displayed in response to completion of the assigned task.

System and method for monitoring battery degradation

A vehicle includes a vehicle system, an outside air temperature (OAT) sensor, a clock, and a controller. The controller includes recorded garage temperatures and actual OAT profiles. At the key-on event, a controller-executed method includes recording an initial temperature reading from the OAT sensor and corresponding time of day from the clock. The controller determines whether the vehicle is in a garage using the profile of recorded temperatures and the time of day, and estimates an OAT for the corresponding time of day when the vehicle is in the garage. An operation of the vehicle system is controlled using the estimated OAT before the vehicle leaves the garage. The system may be a navigation system, and the operation may be modification of a calculated electric vehicle range. The system may be an HVAC or other system in another embodiment.

Vehicle control using an estimated outside air temperature

Methods, systems, and vehicles are provided that provide for estimating a range of a vehicle with a rechargeable energy storage system (RESS). A sensor unit is configured to measure one or more input values pertaining to the RESS. A processor is coupled to the sensor unit, and is configured to determine an amount of energy available from the RESS using the input values and to obtain a time of day. An estimate of the range for the vehicle is determined based on the time of day and the amount of energy available.

Range estimation for a rechargeable energy storage system of a vehicle

A method for predicting the remaining travel distance of an electric vehicle. The method includes determining a useable battery energy value based on battery state-of-charge and battery capacity and a power value needed to heat or cool a vehicle cabin. The method determines an available battery energy value based on the useable battery energy value and an estimated energy value to provide the vehicle cabin heating or cooling, where the estimated energy value is determined using the power value. The method determines a recent energy used value based on an actual recent HVAC energy used value, a recent energy used value with no HVAC system loads and a recent energy used value with maximum HVAC system loads. The method determines a recent distance traveled value and determines the range by dividing the recent distance traveled value by the recent energy used value and multiplying by the available battery energy value.

Electrical vehicle range prediction

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Howell, MI, United States of America

Todd P Lindemann