Secondary battery management
A method of managing a battery system, the battery system including at least one battery cell, at least one sensor configured to measure at least one characteristic of the battery cell, and a battery management system including a microprocessor and a memory, the method comprising receiving by the battery management system, from the at least one sensor at least one measured characteristic of the battery cell at a first time and at least one measured characteristic of the battery cell at a second time. The battery management system estimating, at least one state of the battery cell by applying a physics-based battery model, the physics based battery model being based on differential algebraic equations; and regulating by the battery management system, at least one of charging or discharging of the battery cell based on the at least one estimated state.
1. A method of managing a battery system, the battery system including at least one battery cell, at least one sensor configured to measure at least one characteristic of the battery cell, and a battery management system including a microprocessor and a memory, the method comprising
estimating, by the battery management system, a state of charge of the at least one battery cell at a first time by applying an electrochemical battery model,
wherein applying the electrochemical battery model includes using differential algebraic equations to estimate the state of charge of the at least one battery cell at the first time based at least in part on a previously determined state of charge and a previously determined value of a slowly varying parameter indicative of physical changes to a chemical composition of battery cell materials of the at least one battery cell
wherein applying the electrochemical battery model provides an estimated value of the slowly varying parameter at the first time;
receiving by the battery management system from the at least one sensor, a measured value of at least one measured characteristic of the battery at the first time;
updating, by the battery management system, the state of charge of the at least one battery cell at the first time based on the at least one measured characteristic of the battery at the first time; and
regulating, by the battery management system, at least one of charging or discharging of the battery based on the updated state of charge of the at least one battery cell.
2. The method of claim 1 , wherein estimating the state of charge of the at least one battery cell includes applying an extended Kalman filter.
3. The method of claim 2 , wherein updating the state of charge of the at least one battery cell further includes
determining a noise covariance; and
adjusting a gain of the extended Kalman filter based on the determined noise covariance.
4. The method of claim 3 , further comprising:
estimating a value of the at least one measured characteristic of the battery at the first time using the electrochemical battery model; and
calculating a measurement error based on the measured value of the at least one measured characteristic of the battery at the first time and the estimated value of the at least one measured characteristic of the battery at the first time, and
wherein updating the state of charge of the at least one battery cell further includes updating the state of charge of the at least one battery cell based on the adjusted gain of the extended Kalman filter and the calculated measurement error.
5. The method of claim 4 , wherein adjusting the gain of the extended Kalman filter includes calculating an adjusted gain based on the equation:
K
k
=
COV
t
+
1
t
J
h
T
J
h
COV
t
+
1
t
J
h
T
+
R
wherein K k is the gain of the extended Kalman filter, R is a measure of noise associated with each element of the Kalman filter, J h a Jacobian calculated based on the measurement error, and COV is a calculated covariance matrix of the electrochemical battery model.
6. The method of claim 2 , wherein applying the extended Kalman filter includes using a single extended Kalman filter to jointly estimate both the state of charge of the at least one battery cell and the slowly varying parameter of the at least one battery cell.
7. The method of claim 2 , wherein applying the extended Kalman filter includes using a first extended Kalman filter to estimate the state of charge of the at least one battery cell and using a second extended Kalman filter to separately estimate the slowly varying parameter of the at least one battery cell.
8. The method of claim 1 , wherein regulating the at least one of charging or discharging of the battery based on the state of charge includes regulating a rate of charge or discharge.
9. A battery management system comprising a processor and a memory storing instructions that, when executed by the processor, cause the battery management system to:
estimate a state of charge of the at least one battery cell at a first time by applying an electrochemical battery model,
wherein applying the electrochemical battery model includes using differential algebraic equations to estimate the state of charge of the at least one battery cell at the first time based at least in part on a previously determined state of charge and a previously determined value of a slowly varying parameter indicative of a physical change in a chemical composition of battery cell materials of the at least one battery cell,
wherein applying the electrochemical battery model provides an estimated value of the slowly varying parameter at the first time;
receive a measured value of at least one measured characteristic of the battery from a sensor associated with the at least one battery cell at the first time;
update the state of charge of the at least one battery cell at the first time based on the at least one measured characteristic of the battery at the first time; and
regulate at least one of charging or discharging of the battery based on the updated state of charge of the at least one battery cell.
10. The battery management system of claim 9 , wherein the memory stores instructions that, when executed by the processor, cause the battery management system to estimate the state of charge of the at least one battery cell at the first time by applying an extended Kalman filter.
11. The battery management system of claim 10 , wherein the memory stores instructions that, when executed by the processor, cause the battery management system to update the state of charge of the at least one battery cell by
determining a noise covariance; and
adjusting a gain of the extended Kalman filter based on the determined noise covariance.
12. The battery management system of claim 11 , wherein the memory stores instructions that, when executed by the processor, further cause the battery management system to:
estimate a value of the at least one measured characteristic of the battery at the first time using the electrochemical battery model; and
calculate a measurement error based on the measured value of the at least one measured characteristic of the battery at the first time and the estimated value of the at least one measured characteristic of the battery at the first time, and
wherein the instructions, when executed by the processor, cause the battery system to update the state of charge of the at least one battery cell by updating the state of charge of the at least one battery cell based on the adjusted gain of the extended Kalman filter and the calculated measurement error.
13. The battery management system of claim 12 , wherein the memory stores instructions that, when executed by the processor, cause the battery management system to adjust the gain of the extended Kalman filter by calculating an adjusted gain based on the equation:
K
k
=
COV
t
+
1
t
J
h
T
J
h
COV
t
+
1
t
J
h
T
+
R
wherein K k is the gain of the extended Kalman filter, R is a measure of noise associated with each element of the Kalman filter, J h a Jacobian calculated based on the measurement error, and COV is a calculated covariance matrix of the electrochemical battery model.
14. The battery management system of claim 10 , wherein the memory stores instructions that, when executed by the processor, cause the battery management system to apply the extended Kalman filter by using a single extended Kalman filter to jointly estimate both the state of charge of the at least one battery cell and the slowly varying parameter of the at least one battery cell.
15. The battery management system of claim 10 , wherein the memory stores instructions that, when executed by the processor, further cause the battery management system to apply the extended Kalman filter by using a first extended Kalman filter to estimate the state of charge of the at least one battery cell and using a second extended Kalman filter to separately estimate the slowly varying parameter of the at least one battery cell.
16. The battery management system of claim 9 , wherein the memory stores instructions that, when executed by the processor, cause the battery management system to regulate the at least one of charging or discharging of the battery based on the state of charge includes regulating a rate of charge or discharge.
17. The method of claim 1 , wherein the slowly varying parameter of the at least one battery cell includes at least one selected from a group consisting of cathode particle radius, anode particle radius, ion diffusion rates, intercalation current and transference number, solution conductivity, cell porosity, and equilibrium potential of the anode and cathode.
18. The method of claim 1 , wherein the slowly varying parameter of the at least one battery cell includes at least one selected from a group consisting of
a volume fraction of active material in an anode of the at least one battery cell,
a volume fraction of active material in a cathode of the at least one battery cell,
a total cyclable lithium in the at least one battery cell, and
a radius of particles in one or more electrodes in the at least one battery cell.