Abstract: This invention relates to optical wireless communication systems that use radiation beams for communication. In such systems aiming at the communication partner is not easy especially if the distance between the communication devices is longer and radiation in the non-visible range is used. It is proposed to use an adaptive signal quality indicator in order to provide installing persons and users with a quick and deterministic way of aligning the communication devices.

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: 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 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: Systems/techniques that facilitate peer-to-peer vehicular provision of artificially intelligent traffic analysis are provided. In various embodiments, a system can be onboard a first vehicle. In various aspects, the system can capture, via one or more cameras, one or more microphones, or one or more other sensors of the first vehicle, roadside data associated with a road on which the first vehicle is traveling. In various instances, the system can execute a deep learning neural network on the roadside data, wherein the deep learning neural network can produce as output a classification label, a bounding box, or a pixel-wise segmentation mask localizing an unsafe driving condition along the road. In various cases, the system can transmit, via a peer-to-peer communication link to a second vehicle traveling on the road, an electronic alert based on the unsafe driving condition.

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: 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: 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: 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 monitoring, by a system operatively coupled to a processor, cell states of a plurality of battery cells of a vehicle system, identifying, by the system, based on the cell states, a subset of the plurality of battery cells that is beyond a threshold for remediation, and continuing to use, by the system, 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: 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: 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: Systems/techniques that facilitate peer-to-peer vehicular provision of artificially intelligent traffic analysis are provided. In various embodiments, a system can be onboard a vehicle. In various aspects, the system can receive, via a peer-to-peer communication link, an electronic alert broadcasted by another vehicle traveling on the road. In various instances, the system can determine, via parsing, whether the electronic alert indicates that an unsafe driving condition is located along the road ahead of the vehicle. In various cases, the system can initiate, in response to a determination that the electronic alert indicates that the unsafe driving condition is located along the road ahead of the vehicle, one or more electronic actions based on the unsafe driving condition.

Abstract: Systems/techniques that facilitate peer-to-peer vehicular provision of artificially intelligent traffic analysis are provided. In various embodiments, a system can be onboard a vehicle. In various aspects, the system can capture, via one or more external sensors of the vehicle, roadside data associated with a road on which the vehicle is traveling. In various instances, the system can localize, via execution of a deep learning neural network, an unsafe driving condition along the road based on the roadside data. In various cases, the system can broadcast, via one or more peer-to-peer communication links, an electronic alert regarding the unsafe driving condition to one or more other vehicles traveling on the road.

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: A battery charging system with enhanced time-based charging and coupling detection, as well as battery health monitoring and battery cell selection. A user provides a time available for charging to the vehicle or a mobile device and indirectly to a charging station, or the charging station directly. The time available for charging, as well as battery health information, is shared with the charging station when the charging station detects coupling of the associated connector/coupler to the vehicle, or when the vehicle is detected within a predetermined proximity of the charging station. Subsequently, a standard charging power may be utilized or a charging power may be selected and utilized such that enhanced or optimized charging can be provided for the available time period. A battery control unit identifies degraded or faulted battery cells and charging of these battery cells is avoided or deprioritized.