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: 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: 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: 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: 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: 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: 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: The exemplary systems and methods may generate treatment records for extracorporeal blood treatments. The treatment records may include a plurality of values of various parameters. The various parameters may include a compulsory set of parameters that are preset, a dependent set of parameters that a dependent on one or more of a selected treatment and a system configuration, and a discretionary set of parameters that are selected by a user.

Abstract: A battery module with removable cells and a battery management system or charging station system that determines the number of cells to use for a given trip. Thus, the system receives trip information (starting point, destination, stops, etc.) and determines the number of cells required for the trip, optionally based on traffic conditions, weather, prior driving habits, cell state of charge (SOC), cell state of health (SOH), and the like. The system can recommend which cells to leave in the vehicle, which cells to remove from the vehicle, which cells to place in the vehicle, which cells to charge, and the like, via a visual indicator. Importantly, a number of cells actually required for the trip can be suggested, without extra cells, such that extra weight in terms of unused cells may be removed from the vehicle, thereby increasing trip efficiency.

Abstract: A battery charging system with enhanced time-based charging and coupling detection, as well as battery health monitoring. A user provides a time available for charging to the vehicle, a mobile device, or a charging station directly. In the case that the time available for charging is provided to the vehicle or the mobile device, this information, as well as battery health information, is then 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. When the vehicle is connected to the charging station and charging commences, 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.

Abstract: A battery system that collects and considers multiple variables to provide optimal charge and range information, either on a battery module or per cell basis. This system may provide the required charge time to complete a trip and provides a minimum SOC, which may be achieved by charging or via cell swapping. Variables considered may include expected traffic conditions for a trip, expected temperature for a trip, expected wind conditions for the trip, expected trip topography, etc., all gathered prior to the start of and/or updated during the trip. Based on this smart range estimation, the system can provide the cell swaps required, the number cells to charge, the expected charging time required, and alert the user if charge/swap related stops will be required during the trip in order to successfully complete the trip.

Abstract: A battery charging system with enhanced time-based charging, which may be static or dynamic. A user provides a time available for charging a vehicle to a control unit of the vehicle, a mobile device of the user, or a control unit of a charging station directly. In any case, this information is received by the control unit of the charging station via a wired connection, a wireless connection, a near field connection, a cloud network, or directly. When the vehicle is connected to the charging station and charging commences, a charging power is selected and utilized such that enhanced charging can be provided in the available time period, illustratively providing as much charge as possible in the available time period. This charging power may be modified responsive to user changes to the available charging time while charging is ongoing.

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.