Abstract: A method is provided for automatically generating an augmented model of a cyber-physical component. Cyber-physical components are built from or depend upon the synergy computational and physical components. The method includes: reading an input model (22) into a processor (10), the input model describing a nominal mode of operation for a physical component modeled by the input model; parsing with the processor the input model to generate a parse thereof; analyzing with the processor the parse of the input model; and automatically writing with the processor an augmented model (42) for the physical component from the input model based on the analysis, the augmented model describing the nominal mode of operation for the modeled physical component and at least one alternate mode of operation for the modeled physical component which is different from the nominal mode of operation.

Abstract: A system includes utilizes optical sensors arranged within or on portions of an electrochemical energy device (e.g., a rechargeable Li-ion battery, supercapacitor or fuel cell) to measure operating parameters (e.g., mechanical strain and/or temperature) of the electrochemical energy device during charge/recharge cycling. The measured parameter data is transmitted by way of light signals along optical fibers to a controller, which converts the light signals to electrical data signal using a light source/analyzer. A processor then extracts temperature and strain data features from the data signals, and utilizes a model-based process to detect intercalation stage changes (i.e., characteristic crystalline structure changes caused by certain concentrations of guest species, such as Li-ions, within the electrode material of the electrochemical energy device) indicated by the data features. The detected intercalation stage changes are used to generate highly accurate operating state information (e.g.

Abstract: The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation.

Abstract: A monitoring and management system (MMS) includes one or more fiber optic cables arranged within or on portions of an energy storage device. Each fiber optic cable includes multiple optical sensors. At least one of the optical sensors is configured to sense a parameter of the energy storage device that is different from a parameter of the energy storage device sensed by at least another optical sensor of the multiple optical sensors. The MMS includes a light source configured to provide light to the one or more fiber optic cables and a detector configured to detect light reflected by the optical sensors. The detector generates an electrical signal based on the reflected light. A processor is coupled to receive the electrical signal, to analyze the electrical signal, and to determine state of the energy storage device based on analysis of the electrical signal.

Abstract: A method for automatically detecting a constrained curve over a set of images includes: obtaining a set of one or more binary images of a scene wherein pixels thereof are designated as an edge pixel or not; and, processing at least one of the images. The processing includes: applying a Hough transform to the image to generate an accumulator array; determining Hough peaks from the accumulator array; selecting Hough peaks subject to a set of constraints; determining Hough curve segments for the selected Hough peaks; grouping the Hough curve segments into clusters; selecting from the clusters a cluster having a greatest number of Hough curve segments; and fitting a curve to the Hough curve segments grouped in the selected cluster.

Abstract: A method (300) is provided for classifying an image as being one of a daytime image or a nighttime image. The method includes: obtaining an image to be analyzed (302); determining a first parameter (nH) representative of a first amount of pixels in the image that are sufficiently red or yellow (308); determining a second parameter (nV) representative of a second amount of pixels in the image that are sufficiently light (308); and classifying an image as one of a daytime image or a nighttime image based upon the first and second parameters (310, 312, 314).

Abstract: This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Effects of temperature and load current have also been incorporated into the model. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics for a sample case. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.