AUTOMATIC METHOD FOR ESTIMATING THE STATE OF CHARGE OF A BATTERY CELL
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Abstract
An automatic estimation procedure for estimating a battery'"'"'s state-of-charge includes predicting the state-of-charge using a state model that includes a state-transition matrix. An observation model then uses an observability matrix in connection with predicting a measured value of a parameter. State noise, the covariance of which is characterized by a state-noise covariance matrix contaminates this prediction. Similarly, measurement noise, the covariance of which is characterized by a measurement covariance matrix, contaminates this prediction. The values of these covariance matrices are then adjusted during the estimation procedure.
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Citations
16 Claims
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1-8. -8. (canceled)
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9. A method comprising automatically estimating state-of-charge of a battery'"'"'s cell, wherein automatically estimating state-of-charge comprises, at each of a plurality of instants k, acquiring a measured voltage yk and a measured current ik, wherein said measured voltage is a voltage across said cell'"'"'s terminals, and wherein said measured current is a current selected from the group consisting of a current that charges said cell and a current that discharges said cell, using a state model, calculating, at said instant k, a prediction of said cell'"'"'s state-of-charge SOCk, wherein said state model relates said prediction to a product Fk·
- SOCk−
1 and to a parameter measured at a preceding instant that precedes said instant, wherein said product is a product of said cell'"'"'s state-of-charge at said preceding instant SOCk−
1 and a state transition matrix at said instant Fk, wherein said prediction is corrupted by state noise that is characterized by a state-noise covariance matrix Qk, and wherein said parameter that is measured at a preceding instant is selected from the group consisting of said measured voltage and said measured current, wherein automatically estimating state-of-charge further comprises predicting a covariance of an error in said prediction based at least in part on said state-noise covariance matrix Qk−
1 at said preceding instant k−
1 and a measurement-noise covariance matrix Rk−
1 at said preceding instant k−
1, calculating a prediction ŷ
k of said measured voltage yk at said instant k using an observation model that relates said measured voltage yk to a product Hk·
SOCk of an observability matrix Hk at said instant and said prediction of said cell'"'"'s state-of-charge at said instant, wherein said prediction of said measured voltage ŷ
k is corrupted with measurement noise that is characterized by said measurement noise-covariance matrix at said instant, and correcting said prediction of said state-of-charge SOCk based at least in part on a difference said measured value yk and said prediction ŷ
k of said measured value, wherein automatically estimating said state-of-charge further comprises setting said state-noise covariance matrix Qk using the relationship Qk=[N0G0,k(N0)]−
1 and setting said measurement-noise covariance matrix Rk to be the identity matrix, wherein N0 is a pre-set integer that is greater than unity, and wherein G0,k(N0) is given by - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
- SOCk−
Specification