Gering, KevinL. | Search NASA

DOE OSTI2015

Morpho-Hawk

Morpho-Hawk applies projectional mathematical morphology in a fundamentally new way. Morpho-Hawk extracts object information from digital images by filtering with simple geometrical figures such as rectangles or simple curves. Two core algorithms are used to accomplish this: 1) Object Detection and Feature Extraction from Images: Using projectional morphology, Morpho-Hawk finds features of interest within an image, such as contours, shapes, colors, infrared spectra, and more. Because Morpho-Hawk identifies features based on shape or form, the method can uniquely handle different image or object conditions and directions. MorphoHawk can evaluate all possible images of the analyzed scene using the same transformations that are applied just to one image of that scene. Hence, recognizing a defect within an image or part of an image provides information to assist in recognizing other objects in the image. In addition, known background image information can be morphologically subtracted out in order to focus on the appearance or disappearance of the features of interest. 2) Object Analysis: Upon detection of an object of interest, Morpho-Hawk can analyze the object based learned information from prior images or end-user defined criteria for certain object of special interest by selecting feature (e.g., color, size, shape, apparent volume) and/or symbol (e.g., letter, number). The novel algorithm can analyze targeted objects, even as conditions of such as illumination, shadows, and spectral regions change strongly in following images as compared to the original image. This enables robust recognition, interpolation and prediction. As the analysis shows the presence of these features in the optical signal, the algorithm can make a determination based on user defined probability. The object information can be stored for later analysis using simple morphological data compression methods and/or retention of the original optical images.

Glazoff, MichaelV.↗

DOE OSTI2015

Computer model for electrochemical cell performance loss over time in terms of capacity, power, and conductance (CPC)

Available capacity, power, and cell conductance figure centrally into performance characterization of electrochemical cells (such as Li-ion cells) over their service life. For example, capacity loss in Li-ion cells is due to a combination of mechanisms, including loss of free available lithium, loss of active host sites, shifts in the potential-capacity curve, etc. Further distinctions can be made regarding irreversible and reversible capacity loss mechanisms. There are tandem needs for accurate interpretation of capacity at characterization conditions (cycling rate, temperature, etc.) and for robust self-consistent modeling techniques that can be used for diagnostic analysis of cell data as well as forecasting of future performance. Analogous issues exist for aging effects on cell conductance and available power. To address these needs, a modeling capability was developed that provides a systematic analysis of the contributing factors to battery performance loss over aging and to act as a regression/prediction platform for cell performance. The modeling basis is a summation of self-consistent chemical kinetics rate expressions, which as individual expressions each covers a distinct mechanism (e.g., loss of active host sites, lithium loss), but collectively account for the net loss of premier metrics (e.g., capacity) over time for a particular characterization condition. Specifically, sigmoid-based rate expressions are utilized to describe each contribution to performance loss. Through additional mathematical development another tier of expressions is derived and used to perform differential analyses and segregate irreversible versus reversible contributions, as well as to determine concentration profiles over cell aging for affected Li+ ion inventory and fraction of active sites that remain at each time step. Reversible fade components are surmised by comparing fade rates at fast versus slow cycling conditions. The model is easily utilized for predictive calculations so that future capacity performance can be estimated. The invention covers mathematical and theoretical frameworks, and demonstrates application to various Li-ion cells covering test periods that vary in duration, and shows model predictions well past the end of test periods. Version 2.0 Enhancements: the code now covers path-dependent aging scenarios, wherein the framework allows for arbitrarily-chosen aging conditions over a timeline to accommodate prediction of battery aging over a multiplicity of changing conditions. The code framework also allows for cell conductance and power loss evaluations over cell aging, analysis of series strings that contain a thermal anomaly (hot spot), and evaluation of battery thermal management parameters that impact battery lifetimes. Lastly, a comprehensive GUI now resides in the Ver. 2.0 code.

Gering, KevinL.↗