Apparatus and method for estimating battery state of charge
First Claim
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1. A method for training a support vector machine to determine a present state of charge of an electrochemical cell system comprising:
- choosing a training data;
preprocessing the training data;
finding an optimal parameter of the support vector machine; and
determining support vectors.
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Abstract
A method for training a support vector machine to determine a present state of charge of an electrochemical cell system includes choosing a training data, preprocessing the training data, finding an optimal parameter of the support vector machine, and determining support vectors.
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Citations
32 Claims
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1. A method for training a support vector machine to determine a present state of charge of an electrochemical cell system comprising:
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choosing a training data;
preprocessing the training data;
finding an optimal parameter of the support vector machine; and
determining support vectors. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for estimating a present state of charge of an electrochemical cell using a trained support vector machine, the method comprising:
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preprocessing test data; and
testing an optimized support vector machine using the test data. - View Dependent Claims (12, 13, 14, 15)
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16. A method for estimating a present state of charge of an electrochemical cell system comprising:
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training a support vector machine for a state of charge estimation;
testing the support vector machine; and
calculating an estimate of the present state of charge of the electrochemical cell using the support vector machine. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
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27. An apparatus for estimating the state of charge of an electrochemical cell, the apparatus comprising:
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a sensing component configured to measure a parameter of the electrochemical cell; and
a trained component configured to estimate the state of charge of the electrochemical cell, wherein the trained component is in electrical communication with the sensing component, and the trained component is a support vector machine. - View Dependent Claims (28, 29, 30, 31, 32)
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Specification