Abstract: In one or more embodiments described herein, devices, systems, methods and/or apparatuses are described that can facilitate locational awareness and/or guidance of an inductive charging element relative to a target charging station. A device can comprise an inductive charging element, a signal component located relative to the inductive charging element for common movement with the inducting charging element, wherein the signal component transmits or receives a signal from a target position, and a controller that determines a distance between the inductive charging element and the target position based on a time measurement of the signal. The time measurement can include a time of arrival measurement and/or a time of flight measurement. The signal component can be an ultrawideband antenna, laser doppler device, acoustic device, electromagnetic device and/or other transmitter and/or receiver.

Abstract: One or more embodiments described herein can facilitate electric charge transfer to/from one or more battery cells and/or multi-cell battery packs of an electric vehicle. An exemplary method can comprise charging, by a system operatively coupled to a processor, a primary power source of a first vehicle using a primary power source of a second electric vehicle. The charge can comprise a physical coupling or a wireless charge between the primary power sources of the first electric vehicle and the second electric vehicle.

Abstract: Various embodiments and approaches are described to minimize degradation of respective modules combined to form a battery pack onboard an electric vehicle (EV). Systems and components are presented to determine a respective operational state of the modules, and based thereon, a first subset of modules can be selected to provision power to various EV components while a second subset of modules can be deselected. Module selection can be based upon a threshold operating condition. A visual representation of the modules and their respective operational state can be presented, in conjunction with one or more alarms and recommended corrective operations. Artificial intelligence methods can be utilized to determine an operational state of a module(s). A module can be scheduled for replacement. Limiting degradation to a first module can minimize degradation of a second module. The various components can be stored in a memory and executed by a processor.

Abstract: One or more embodiments described herein can facilitate electric charge transfer to/from one or more battery cells and/or multi-cell battery packs of an electric vehicle that is moving. An exemplary method can comprise wirelessly charging, by a system operatively coupled to a processor, a power source of a first electric vehicle by a power source of a second electric vehicle while the first electric vehicle and the second electric vehicle are moving. The power source of the first electric vehicle can be a primary power source of the first electric vehicle, and the power source of the second electric vehicle can be a primary power source of the second electric vehicle.

Abstract: One or more embodiments described herein can facilitate electric charge transfer to/from one or more battery cells and/or multi-cell battery packs of an electric vehicle from another electric vehicle, based at least in part on state of charge and/or state of health monitoring at one or more of the cell-level or pack-level. An exemplary method can comprise identifying, by a system operatively coupled to a processor, a subset of a plurality of battery cells of a vehicle system, wherein the subset of the plurality of battery cells is beyond a threshold for remediation, and initiating a charge, by the system, of the subset of the plurality of battery cells, wherein the charge comprises energy transfer at the subset of the plurality of battery cells such that the subset of the plurality of battery cells degrades towards end of life of the subset of the plurality of battery cells.

Abstract: One or more embodiments described herein can facilitate messaging to direct electric charge transfer to/from one or more battery cells and/or multi-cell battery packs of an electric vehicle. An exemplary method can comprise initiating, by a system operatively coupled to a processor, a communication between a first electric vehicle and second electric vehicle to coordinate provisioning of an emergency charging between the first electric vehicle and the second electric vehicle. The power source of the first electric vehicle can be a primary power source of the first electric vehicle, and the power source of the second electric vehicle can be a primary power source of the second electric vehicle.

Abstract: The navigation system of the present disclosure is configured to receive a short command from a vehicle occupant and extrapolate relevant details associated with the command using an artificial intelligence (AI)/machine learning (ML) algorithm. These relevant details may be obtained from a mobile device associated with the occupant and in communication with the navigation system, an external database in communication with the navigation system, etc. Communication links may be wired, wireless through the cloud, near field, etc. Further, each occupant's identity may be determined via near field key fob recognition, microphone voice recognition, camera facial recognition, or via the detection of the mobile device associated with the occupant. Levels of complexity may be added, with multiple occupants being identified and issuing different commands, multiple external information sources subsequently being consulted, and multiple sub-routes to multiple destinations being planned and followed.

Abstract: Various embodiments and approaches are described to minimize degradation of respective modules combined to form a battery pack onboard an electric vehicle (EV). Systems and components are presented to determine a respective operational state of the modules, and based thereon, a first subset of modules can be selected to provision power to various EV components while a second subset of modules can be deselected. Module selection can be based upon a threshold operating condition. A visual representation of the modules and their respective operational state can be presented, in conjunction with one or more alarms and recommended corrective operations. Artificial intelligence methods can be utilized to determine an operational state of a module(s). A module can be scheduled for replacement. Limiting degradation to a first module can minimize degradation of a second module. The various components can be stored in a memory and executed by a processor.

Abstract: One or more embodiments described herein can facilitate electric charge transfer to/from one or more battery cells and/or multi-cell battery packs of an electric vehicle from a second electric vehicle, based at least in part on state of charge and/or state of health monitoring at one or more of the cell-level or pack-level. An exemplary method can comprise identifying, by a system operatively coupled to a processor, based on a comparison of a metric to a historical metric for vehicle performance, a current event that is defined by the metric as leading to degradation of a plurality of battery cells of a vehicle system, upon identifying the current event, determining, by the system, a subset of the plurality of battery cells that is beyond a threshold for remediation, and continuing to use, by the system, the subset such that the subset degrades towards end of life of the subset.

Abstract: Various embodiments and approaches are described to minimize degradation of respective modules combined to form a battery pack onboard an electric vehicle (EV). Systems and components are presented to determine a respective operational state of the modules, and based thereon, a first subset of modules can be selected to provision power to various EV components while a second subset of modules can be deselected. Module selection can be based upon a threshold operating condition. A visual representation of the modules and their respective operational state can be presented, in conjunction with one or more alarms and recommended corrective operations. Artificial intelligence methods can be utilized to determine an operational state of a module(s). A module can be scheduled for replacement. Limiting degradation to a first module can minimize degradation of a second module. The various components can be stored in a memory and executed by a processor.

Abstract: Automated vehicle battery health optimization (e.g., using a computerized tool) is enabled. For example, a system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a battery health component that, using a defined battery health algorithm and a battery sensor, determines degradation of a battery of a vehicle, and an execution component that, based on the degradation of the battery and a traveling direction of a user of the vehicle, external to the vehicle, facilitates an action determined to mitigate further degradation of the battery.

Abstract: Vehicle battery health optimization and communication (e.g., using a computerized tool) are enabled. For example, a system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a battery health component that, using a defined battery health algorithm and a battery sensor, determines degradation of a battery of a vehicle, and a communication component that, based on the degradation of the battery and a traveling direction of a user of the vehicle, external to the vehicle, transmits a message representative of the degradation of the battery.

Abstract: Various embodiments and approaches are described to minimize degradation of respective modules combined to form a battery pack onboard an electric vehicle (EV). Systems and components are presented to determine a respective operational state of the modules, and based thereon, a first subset of modules can be selected to provision power to various EV components while a second subset of modules can be deselected. Module selection can be based upon a threshold operating condition. A visual representation of the modules and their respective operational state can be presented, in conjunction with one or more alarms and recommended corrective operations. Artificial intelligence methods can be utilized to determine an operational state of a module(s). A module can be scheduled for replacement. Limiting degradation to a first module can minimize degradation of a second module. The various components can be stored in a memory and executed by a processor.

Abstract: A method and system for collecting evidence following a vehicle incident. The system is operable for detecting and determining a direction associated with the vehicle incident and automatically and contemporaneously requesting and receiving related information from the vehicle itself, potentially from any other vehicle involved, and/or from any surrounding devices, including vehicles, mobile devices, and/or infrastructure devices, that may have captured the vehicle incident.

Abstract: Various embodiments and approaches are described to minimize degradation of respective modules combined to form a battery pack onboard an electric vehicle (EV). Systems and components are presented to determine a respective operational state of the modules, and based thereon, a first subset of modules can be selected to provision power to various EV components while a second subset of modules can be deselected. Module selection can be based upon a threshold operating condition. A visual representation of the modules and their respective operational state can be presented, in conjunction with one or more alarms and recommended corrective operations. Artificial intelligence methods can be utilized to determine an operational state of a module(s). A module can be scheduled for replacement. Limiting degradation to a first module can minimize degradation of a second module. The various components can be stored in a memory and executed by a processor.

Abstract: Various embodiments and approaches are described to minimize degradation of respective modules combined to form a battery pack onboard an electric vehicle (EV). Systems and components are presented to determine a respective operational state of the modules, and based thereon, a first subset of modules can be selected to provision power to various EV components while a second subset of modules can be deselected. Module selection can be based upon a threshold operating condition. A visual representation of the modules and their respective operational state can be presented, in conjunction with one or more alarms and recommended corrective operations. Artificial intelligence methods can be utilized to determine an operational state of a module(s). A module can be scheduled for replacement. Limiting degradation to a first module can minimize degradation of a second module. The various components can be stored in a memory and executed by a processor.

Abstract: Various embodiments and approaches are described to minimize degradation of respective modules combined to form a battery pack onboard an electric vehicle (EV). Systems and components are presented to determine a respective operational state of the modules, and based thereon, a first subset of modules can be selected to provision power to various EV components while a second subset of modules can be deselected. Module selection can be based upon a threshold operating condition. A visual representation of the modules and their respective operational state can be presented, in conjunction with one or more alarms and recommended corrective operations. Artificial intelligence methods can be utilized to determine an operational state of a module(s). A module can be scheduled for replacement. Limiting degradation to a first module can minimize degradation of a second module. The various components can be stored in a memory and executed by a processor.

Abstract: A method and system for collecting and reporting evidence following a vehicle incident. The system is operable for detecting and determining a direction associated with the vehicle incident and automatically and contemporaneously requesting and receiving related information from the vehicle itself, potentially from any other vehicle involved, and/or from any surrounding devices, including vehicles, mobile devices, and/or infrastructure devices, that may have captured the vehicle incident.

Abstract: A method and system for providing evidence following a vehicle incident. The system is operable for detecting and determining a direction associated with the vehicle incident and automatically and contemporaneously requesting and receiving related information from the vehicle itself, potentially from any other vehicle involved, and/or from any surrounding devices, including vehicles, mobile devices, and/or infrastructure devices, that may have captured the vehicle incident. The method and system also involve receiving a request for providing an image/video, generating and/or previewing the image/video, and subsequently providing the image/video.

Abstract: The navigation system of the present disclosure is configured to receive a short command from a vehicle occupant and extrapolate relevant details associated with the command using an artificial intelligence (AI)/machine learning (ML) algorithm. These relevant details may be obtained from a mobile device associated with the occupant and in communication with the navigation system, an external database in communication with the navigation system, etc. Communication links may be wired, wireless through the cloud, near field, etc. Further, each occupant's identity may be determined via near field key fob recognition, microphone voice recognition, camera facial recognition, or via the detection of the mobile device associated with the occupant. Levels of complexity may be added, with multiple occupants being identified and issuing different commands, multiple external information sources subsequently being consulted, and multiple sub-routes to multiple destinations being planned and followed.