Abstract: Systems, methods, and devices are provided for estimating a state-of-charge in an energy storage system. The systems, methods, and devices may measure a current and a voltage related to the energy storage system, generate a model-based state-of-charge estimate based thereon, generate an aggregated state-of-charge estimate based on the model-based state-of-charge estimate, generate an adaptation current based on the measured current, aggregated state-of-charge estimate, and the adaptive state-of-charge estimate, and update the adaptive state-of-charge estimate based on the measured current and the adaptation current. The systems, methods, and devices may also provide a tracking indication whether the adaptive state-of-charge estimate is tracking between upper and lower confidence limits.

Abstract: Systems, methods, and devices are provided for estimating a state-of-charge in an energy storage system. The systems, methods, and devices may measure a current and a voltage related to the energy storage system, generate a model-based state-of-charge estimate based thereon, generate an aggregated state-of-charge estimate based on the model-based state-of-charge estimate, generate an adaptation current based on the measured current, aggregated state-of-charge estimate, and the adaptive state-of-charge estimate, and update the adaptive state-of-charge estimate based on the measured current and the adaptation current. The systems, methods, and devices may also provide a tracking indication whether the adaptive state-of-charge estimate is tracking between upper and lower confidence limits.

Abstract: Systems, methods, and devices are provided for estimating a state-of-health (SOH) in an energy storage system, where the estimate accounts for both power fade and capacity fade. The SOH estimation system comprises a SOH estimation module configured to receive, and to determine the estimate of the SOH based on: a nominal capacity input; a voltage input; a current input; a nominal open circuit voltage curve; an operational resistance model; a nominal resistance model; an operational dynamic model; and a rated discharge current. The SOH estimation module may further comprise a SOH aggregation module configured to receive a power rating estimate, PRn, and a normalized capacity estimate, NCn, and may be configured to determine the estimate of the SOH based on the power rating estimate, PRn, and the normalized capacity estimate, NCn.

Abstract: Systems, methods, and devices are provided for estimating a state-of-health (SOH) in an energy storage system, where the estimate accounts for both power fade and capacity fade. The SOH estimation system comprises a SOH estimation module configured to receive, and to determine the estimate of the SOH based on: a nominal capacity input; a voltage input; a current input; a nominal open circuit voltage curve; an operational resistance model; a nominal resistance model; an operational dynamic model; and a rated discharge current. The SOH estimation module may further comprise a SOH aggregation module configured to receive a power rating estimate, PRn, and a normalized capacity estimate, NCn, and may be configured to determine the estimate of the SOH based on the power rating estimate, PRn, and the normalized capacity estimate, NCn.

Abstract: Systems, methods, and devices are provided for estimating a state-of-health (SOH) in an energy storage system, where the estimate accounts for both power fade and capacity fade. The SOH estimation system comprises a SOH estimation module configured to receive, and to determine the estimate of the SOH based on: a nominal capacity input; a voltage input; a current input; a nominal open circuit voltage curve; an operational resistance model; a nominal resistance model; an operational dynamic model; and a rated discharge current. The SOH estimation module may further comprise a SOH aggregation module configured to receive a power rating estimate, PRn, and a normalized capacity estimate, NCn, and may be configured to determine the estimate of the SOH based on the power rating estimate, PRn, and the normalized capacity estimate, NCn.

Abstract: Systems, methods, and devices are provided for estimating a state-of-charge in an energy storage system. The systems, methods, and devices may measure a current and a voltage related to the energy storage system, generate a model-based state-of-charge estimate based thereon, generate an aggregated state-of-charge estimate based on the model-based state-of-charge estimate, generate an adaptation current based on the measured current, aggregated state-of-charge estimate, and the adaptive state-of-charge estimate, and update the adaptive state-of-charge estimate based on the measured current and the adaptation current. The systems, methods, and devices may also provide a tracking indication whether the adaptive state-of-charge estimate is tracking between upper and lower confidence limits.

Abstract: Systems, methods, and devices are provided for estimating a state-of-charge in an energy storage system. The systems, methods, and devices may measure a current and a voltage related to the energy storage system, generate a model-based state-of-charge estimate based thereon, generate an aggregated state-of-charge estimate based on the model-based state-of-charge estimate, generate an adaptation current based on the measured current, aggregated state-of-charge estimate, and the adaptive state-of-charge estimate, and update the adaptive state-of-charge estimate based on the measured current and the adaptation current. The systems, methods, and devices may also provide a tracking indication whether the adaptive state-of-charge estimate is tracking between upper and lower confidence limits.

Abstract: Systems, methods, and devices are provided for estimating a state-of-charge in an energy storage system. The systems, methods, and devices may measure a current and a voltage related to the energy storage system, generate a model-based state-of-charge estimate based thereon, generate an aggregated state-of-charge estimate based on the model-based state-of-charge estimate, generate an adaptation current based on the measured current, aggregated state-of-charge estimate, and the adaptive state-of-charge estimate, and update the adaptive state-of-charge estimate based on the measured current and the adaptation current. The systems, methods, and devices may also provide a tracking indication whether the adaptive state-of-charge estimate is tracking between upper and lower confidence limits.

Abstract: A television visual artwork generation system and method is provided that converts a television signal into a pattern/artwork that is displayed on a display.

Abstract: A television visual artwork generation system and method is provided that converts a television signal into a pattern/artwork that is displayed on a display.

Abstract: A method and apparatus for reprogramming a digital electronic device or Appliance is provided. A preferred embodiment locates a reprogrammable Controller chip in the communications flow between a reprogrammable digital device and an application server. The application server transmits commands and programming data to the Controller chip in data packets via the Internet. The Controller sniffs each data packet to determine the type of data contained therein. The Controller will immediately forward certain types of data packet onto the digital device. Alternatively, the Controller may store data packets that relate to the reprogramming of either the digital device or the Controller itself, for collation and later transference during a reprogramming of the digital device or the Controller. Data packets may contain upgrade data, status queries, monitoring instructions, logic settings, and other information relating to the operation and status of the server, device or Controller.