Dynamic battery capacity estimation

1. A method of determining a state of health of a battery in real time, the method comprising:

estimating a parameter value associated with the state of health of the battery;

determining one or more of a terminal voltage, an accumulated charge, a state of charge, and a temperature of the battery;

determining in real time, via a computing device, a reserve capacity of the battery based at least in part on the estimated parameter value and one or more of the terminal voltage, the accumulated charge, the state of charge, and the temperature of the battery; and

determining a signal excitation level based on one or more of the terminal voltage, the terminal current, the state of charge, and the temperature of the battery;

comparing the signal excitation level to a predetermined threshold; and

updating the parameter value if the signal excitation level is above the predetermined threshold.

2. A method as set forth in claim 1 , wherein determining the reserve capacity includes:

determining a change in an open circuit voltage over time, wherein the open circuit voltage is derived from the terminal voltage;

determining a change in a state of charge of the battery over time; and

determining the reserve capacity based at least in part on the change in the open circuit voltage, the change in the state of charge, and the parameter value.

3. A method as set forth in claim 2 , wherein determining the reserve capacity includes identifying a relationship between the change in the open circuit voltage and the change in the state of charge of the battery.

4. A method as set forth in claim 3 , wherein identifying the relationship includes accessing a ratio representing the relationship from a look-up table.

5. A method as set forth in claim 1 , further comprising storing at least one of the estimated parameter value and the updated parameter value in a memory device.

6. A method as set forth in claim 1 , wherein updating the parameter value includes:

setting an initial parameter value; and

applying, via the computing device, a Recursive Least Squares procedure to the initial parameter value.

7. A method as set forth in claim 6 , wherein applying the Recursive Least Squares procedure includes generating a parameter vector that includes an initial parameter value for a plurality of parameters.

8. A method as set forth in claim 7 , wherein applying the Recursive Least Squares procedure includes generating a covariance matrix, wherein the covariance matrix includes a positive definite matrix.

9. A method as set forth in claim 8 , wherein applying the Recursive Least Squares procedure includes:

measuring one or more of the terminal voltage and a terminal current at a plurality of time steps, wherein the accumulated charge is derived from the terminal current; and

generating a signal vector that includes one or more of the measured terminal voltage and the measured terminal current for each time step.

10. A method as set forth in claim 9 , wherein applying the Recursive Least Squares procedure includes multiplying, via the computing device, the covariance matrix with the signal vector to define a Kalman gain.

11. A method as set forth in claim 10 , wherein applying the Recursive Least Squares procedure includes updating the parameter values in the parameter vector based at least in part upon the Kalman gain.

12. A method as set forth in claim 1 , further comprising generating, via the computing device, a signal representing the reserve capacity of the battery.

13. A method as set forth in claim 12 , further comprising determining a battery life based at least in part on the battery capacity.

14. A vehicle comprising:

a battery;

at least one sensor configured to measure at least one of a terminal voltage, a terminal current, and a temperature of the battery;

a computing device in communication with the at least one sensor and configured to determine an accumulated charge, a state of charge of the battery, and a parameter value associated with a state of health of the battery;

wherein the computing device is further configured to determine a reserve capacity of the battery in real time based at least in part on a parameter value and one or more of the terminal voltage, the accumulated charge, the state of charge, and the temperature of the battery; and

wherein the computing device is configured to determine a signal excitation level based on one or more of the measured terminal voltage, the measured terminal current, the determined state of charge, and the measured temperature of the battery, and wherein the computing device is further configured to compare the signal excitation level to a predetermined threshold and update the parameter value if the signal excitation level is above the predetermined threshold.

15. A vehicle as set forth in claim 14 , wherein the computing device is configured to determine a change in the open circuit voltage over time from the terminal voltage, determine a change in the state of charge of the battery over time, and determine the reserve capacity based at least in part on the change in the open circuit voltage, the change in the state of charge, and the parameter value.

16. A vehicle as set forth in claim 14 , wherein the computing device is configured to estimate the parameter value by setting an initial parameter value and applying a Recursive Least Squares procedure to the initial parameter value.

17. A vehicle as set forth in claim 14 , wherein the computing device is configured to generate a signal representing the reserve capacity.

18. A non-transitory computer-readable medium tangibly embodying computer-executable instructions comprising:

estimating a parameter value associated with the state of health of the battery;

determining one or more of a terminal voltage, an accumulated charge, a state of charge, and a temperature of the battery;

determining, via a computing device, a reserve capacity of the battery based at least in part on the estimated parameter value and one or more of the terminal voltage, the accumulated charge, the state of charge, and the temperature of the battery; and

determining a signal excitation level based on one or more of the terminal voltage, the terminal current, the state of charge, and the temperature of the battery;

comparing the signal excitation level to a predetermined threshold; and

updating the parameter value if the signal excitation level is above the predetermined threshold.