Gm Global Technolgoy Operations, Inc.

Aalborg University

Vladimir Suplin

Klaus Trangbaek

Avshalom Suissa

Daniel Urieli

Moshe Laifenfeld

Peter Omand Rasmussen

Nick Ilso Berg

Rasmus Koldborg Holm

Jeremie Benichou

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 tuning a trajectory tracking controller for a vehicle includes a trajectory planner configured to generate the planned trajectory and to output one or more planned trajectory components representative of the planned trajectory, a model predictive controller including an internal model and an optimizer, and a tuning neural network configured to receive the one or more planned trajectory components and one or more measured trajectory components and to produce weights for a cost function. The internal model is configured to receive a predicted control input from the optimizer and the one or more measured trajectory components and to produce a predicted output. The optimizer utilizes a cost function and is configured to receive the weights for the cost function and a predicted error and to produce the predicted control input, wherein the predicted error is a selected one of the planned trajectory components minus the predicted output.

System for tuning a trajectory tracking controller for an automotive vehicle

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.

Adaptive in-drive updating of vehicle energy consumption prediction

A vehicle control system for automated driver-assistance includes a model-based controller that generates a first control signal to alter an operation of a plurality of actuators of a vehicle based on a reference trajectory for the vehicle, and a present state of the vehicle and actuators. The vehicle control system further includes a neural network controller that generates a second control signal to alter the operation of the actuators of the vehicle based on a reference trajectory for the vehicle, and the present state of the vehicle and actuators. The vehicle control system further includes a combination module that combines the first control signal and the second control signal to operate the actuators based on a combined signal.

Vehicle control using neural network controller in combination with model-based controller

A non-linear stiffness actuator for a suspension corner employed in a vehicle includes an actuator housing and an actuator shaft configured to transmit an actuator force to the road wheel. The actuator also includes a primary elastic member assembly arranged between the actuator housing and the actuator shaft and configured to exert a primary elastic member force along the actuator shaft. The actuator additionally includes a secondary elastic member assembly configured to exert a variable secondary elastic member force acting between the actuator housing and the actuator shaft. The variable secondary elastic member force is configured to selectively contribute to and subtract from the primary elastic member force to thereby facilitate the non-linear stiffness of the actuator. A vehicle having such a non-linear stiffness actuator is also provided.

Non-linear stiffness actuator for vehicle suspension

A strut assembly for a suspension corner employed in a vehicle having a vehicle body and a road wheel includes a damper. The strut assembly also includes an elastic unit having at least one spring module acting in concert with the damper to suspend the vehicle body relative to the road wheel. Each spring module has a positive stiffness spring arranged in parallel with a negative stiffness spring. A vehicle that has a suspension corner employing the elastic unit and is configured to maintain contact between the road wheel and the road surface and provide isolation of vibration between the road wheel and the vehicle body is also contemplated.

Spring module with negative and positive stiffness springs

A spring-damper assembly for a suspension corner employed in a vehicle having a vehicle body and a road wheel includes a fluid spring configured to suspend the vehicle body relative to the road wheel. The spring-damper assembly also includes a damper configured to attenuate compression and rebound oscillations of the fluid spring. The spring-damper assembly additionally includes a spring-seat housing configured to retain the fluid spring and establish a position of the fluid spring relative to the damper. The spring-seat housing includes an inner surface defining a contour configured to guide the fluid spring upon compression thereof around the damper and define a non-linear stiffness of the fluid spring. A vehicle having such a spring-damper assembly is also provided.

Spring-damper assembly

A system and method for wirelessly controlling a shape memory alloy (SMA) actuator using magnetic resonant coupling (MRC). The SMA actuator is part of a receiver circuit including an actuator coil, where the SMA actuator is configured into a certain shape. The system includes a transmitter circuit having a transmitter coil and a controller, where the transmitter coil receives an AC current that causes the transmitter coil to generate an oscillating magnetic field in resonance with the actuator coil in the receiver circuit and be magnetically coupled thereto. The current induced in the actuator coil creates heat that reconfigures the SMA actuator to provide the actuation.

Apparatus for cost effective wireless actuator using SMA and MRC

An apparatus for controlling force of a magnetic lead screw actuator includes a magnetic lead screw actuator, an external control module and at least one sensor device integrated within the magnetic lead screw actuator. The magnetic lead screw actuator includes an electric machine, a rotor, and a translator. The rotor includes a rotor magnet assembly forming first helical magnetic threads along the rotor and the translator includes a translator magnet assembly forming second helical magnetic threads along the translator. Rotation of the rotor by the electric machine effects linear translation of the translator by interaction of the first and second helical magnetic threads. The external control module is electrically operatively coupled to an electric machine controller of the magnetic lead screw actuator. The at least one sensor device integrated within the magnetic lead screw actuator is configured to measure a parameter indicative of a relative displacement between the rotor and the translator and this parameter is provided as feedback to the electric machine controller.

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Modiin, Israel

Vladimir Suplin