Gm Global Technolgoy Operations, Inc.

The University of Waterloo

Vyacheslav Berezin

Shaun S Marshall

Norman Joseph Weigert

Moshe Laifenfeld

Alaa M Khamis

Ramzi Abdelmoula

Timothy J Talty

Amanda Kalhous

Kobi Jacob Scheim

Igal Kotzer

Safieddin Safavi-Naeini

Nikola J Pudar

Michael J Chappell

Roddi L MacInnes

Kier M Mathieson

Gholamreza Zeinolabedin Rafi

Arief Barkah Koesdwiady

Paul A Donald

Hannah Nissenbaum

Richard C Spina

Caroline Chaplow

Pedro E Villanueva Pena

Noah Samuel Louis Segal

A method for dynamically matching energy demand of a population of electric vehicles (EVs) with energy supply of a population of charging stations within a geofenced perimeter includes receiving, via a cloud-based server, EV information from each respective EV, and receiving charging station information from each respective charging station. The method includes generating an SoC map and a charging station power map from the EV information and the charging station information, respectively, and predicting the energy supply and demand using the maps. The server dynamically matches the EVs to at least one of the charging stations or vice versa using a reward function, the predicted energy supply, and the predicted energy demand, including generating a rank-ordered listing for each of the EVs and/or each of the charging stations in a manner that maximizes an expected discounted future reward.

Dynamic multiple bi-directional supply and demand matching for EV charging

Presented are intelligent charging systems for transferring energy to and from motor vehicles, methods for making/operating such systems, and vehicles with V2V and V2G energy transfer capabilities. A method for operating an intelligent charging system includes a system controller receiving, from a user interface of a user, a request to schedule a transfer of electricity to the user. The system controller broadcasts to a crowdsourced set of service providers a solicitation for energy transfer bids for completing the user's transfer request. After receiving multiple bid submissions for completing the transfer request from multiple service providers, the system controller selects an optimized one of the bid submissions using a multi-criteria selection strategy. The multi-criteria selection strategy includes a joint utility maximization function and/or a trade-off Pareto solution. The system controller transmits to the service provider associated with the optimized bid submission a task allocation with instructions to provide the transfer request.

Intelligent charging systems and control logic for crowdsourced vehicle energy transfer

A method for brand identification of an electric vehicle to a charging station includes connecting a plug of the charging station to a socket of the electric vehicle, receiving a control pilot signal at the socket from the charging station, terminating the control pilot signal in a variable resistance in the electric vehicle, and toggling the variable resistance in a vehicle pattern that transitions between two charging state resistances. The charging station detects the vehicle pattern and maps the vehicle pattern to a vehicle brand of the electric vehicle. The method includes receiving electrical current to recharge the electric vehicle at the socket.

Brand identification while recharging an electric vehicle

In accordance with various embodiments, methods, systems, and vehicles are provided for determining an environment of vehicles. In one embodiment, a vehicle includes a body, a plurality of sensors, and a processor. The plurality of sensors are disposed onboard the vehicle, and is configured to at least facilitate transmitting signals from a vehicle and receiving return signals at the vehicle after the transmitted signals have contacted one or more objects. The processor is disposed onboard the vehicle, and is coupled to the plurality of sensors. The processor is configured to at least facilitate identifying one or more parameters of the return signals; comparing the one or more parameters with historical data stored in a memory; and determining an environment of the vehicle based at least in part on the comparison of the one or more parameters with the historical data.

Self-learning system for reflective environments

A method of operating a passive entry passive start (PEPS) system includes authenticating the mobile wireless device, detecting a presence of the mobile wireless device in a specified location in an interior of the vehicle, and calibrating one or more operating parameters of the PEPS system based on radio frequency (RF) performance characteristics of the mobile wireless device.

Vehicle PEPS system calibration using a mobile device

A system is provided including a data module, a classification module and a control module. The data module is configured to receive at least one of acceleration data and gravity data from an accelerometer or a mobile network device, where the acceleration data and the gravity data are indicative of accelerations experienced by the mobile network device. The classification module is configured to classify a location of the mobile network device on a person based on the at least one of the acceleration data and the gravity data and generate a location classification output. The control module is configured to perform an operation based on the location classification output.

Acceleration and gravity data based system and method for classifying placement of a mobile network device on a person

A keyless entry and start system for a vehicle includes a vehicle subsystem configured to control at least one of access to an interior compartment of the vehicle and starting of the vehicle. The system further includes an actuator movable between first and second positions and a switch coupled to the actuator. The switch assumes a first state when the actuator is in the first position and a second state when the actuator is in the second position. The system further includes a controller coupled to the switch and configured to detect an actuation sequence of the actuator, the actuation sequence corresponding to an access code. The controller is further configured to compare the access code to an authorization code and generate a control signal configured to cause the vehicle subsystem to switch states when the entry code corresponds to the authorization code.

Keyless control system

A system and method of locating a key fob with respect to a vehicle includes: detecting short-range wireless signals communicated between the key fob and a plurality of nodes at the vehicle using an IEEE 802.11 protocol; calculating the distance of the key fob relative to each of the nodes attached to the vehicle based on the detected short-range wireless signal; and determining the location of the key fob based on the distance of the key fob relative to each of the nodes.

Methods for locating a vehicle key fob

A system and method of locating a key fob with respect to a vehicle includes: communicating between a plurality of directional sensors used by the vehicle and a key fob via a short-range wireless signal; generating signal data at the plurality of directional sensors or the key fob indicative of one or more attributes of the short-range wireless signal; generating location information based on the one or more attributes by processing the signal data generated by the plurality of directional sensors or the key fob; and determining the location of the key fob relative to the vehicle based on the generated location information.

Vehicle PEPS system using directional sensors

A multi-slot patch antenna is provided. The multi-slot patch antenna includes a central patch including cut corners; a plurality of strips of varying widths, the plurality of strips surrounding the central patch; and a plurality of slots of varying widths, the plurality of slots being positioned between each of the plurality of strips, wherein one of the plurality of slots is positioned between a first one of the plurality of strips and the central patch.

Miniature patch antenna

Data acquisition from a sampling of vehicle sensors includes identifying a vehicle population density for a defined region, calculating a proportional representation ratio from the vehicle population density, and transmitting a request for data over a network. The request includes the response criteria configured with the proportional representation ratio. The data acquisition also includes receiving the data from vehicles that are located in the defined region and that fall within the proportional representation ratio, and which meet the response criteria.

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Newmarket, Canada

Vyacheslav Berezin