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Model-based prognostics for batteries which estimates useful life and uses a probability density function

  • US 8,332,342 B1
  • Filed: 11/19/2009
  • Issued: 12/11/2012
  • Est. Priority Date: 11/19/2009
  • Status: Active Grant
First Claim
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1. A method for estimating remaining useful life (RUL) of a battery during discharge of the battery, the method comprising providing a computer system that is programmed:

  • to provide or receive a quantitative empirical model with at least one associated model parameter for at least one electro-chemical process that is active during discharge of the battery;

    to receive and use measured values provided by one or more sensors for at least one electro-chemical process that is active during discharge of the battery;

    to receive and use training data comprising at least one of;

    at least one operating, condition for the battery, at least one sensor measurement value for battery operation, and at least one ground truth attribute for battery discharge;

    to compute and incorporate at least one numerical parameter value for the electro-chemical process that characterizes battery discharge behavior;

    to identify at least one uncertainty in the quantitative model, including an uncertainty range for the at least one model parameter and an uncertainty range for the at least one measured sensor value;

    to provide and incorporate at least one numerical value for at least one probability density function (pdf) corresponding to a distribution of the at least one uncertainty;

    to provide at least one process model of at least one process component with at least one estimate of an value of a probability density function (pdf) for a distribution of at least one uncertainty in the at least one process model, to provide a characterization of battery discharge;

    to provide or receive run-time data, including the at least one battery operating condition and at least one sensor measurement value; and

    to apply the quantitative model of the battery in a particle filtering framework to estimate at least one battery discharge variable of interest, comprising at least one of state of charge (SOC) and terminal voltage of the battery, and to contemporaneously modify the at least one model parameter value used in the quantitative model.

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