Method of Estimating Battery State of Charge

• US 20110031938A1
• Filed: 08/04/2009
• Published: 02/10/2011
• Est. Priority Date: 08/04/2009
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Ishikawa, Yosuke
• Granted Patent: US 8,207,706 B2
• Time in Patent Office: Days
• US Class Current: 320/162
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/3842 combining voltage and current measurements

Abstract

The present teachings are directed toward a machine implemented method for estimating the state of charge of a battery. The machine implemented method includes providing measured and estimated cell terminal voltage to a model coefficient updater to update a model coefficient. Battery current information is provided to a battery state of charge estimator along with the updated model coefficient so that the estimated state of charge can be determined. A multi-layer model can be utilized to determine the states of charge for layers of the electrodes. The method can be implemented on a processing device, and is particularly applicable to Li-ion batteries.

Claims

1. A machine implemented method for estimating the state of charge of a battery, the machine implemented method comprising:

providing a measured cell terminal voltage and an estimated cell terminal voltage to a model coefficient updater;

updating a model coefficient by utilizing the measured cell terminal voltage and estimated cell terminal voltage;

providing battery current and measured cell terminal voltage to a battery state of charge estimator;

providing the updated model coefficient to the battery state of charge estimator, and

determining an estimated state of charge of the battery and an estimated cell terminal voltage by utilizing the updated model coefficient, the battery current information, and the measured cell terminal voltage,

wherein the method is implemented on a processing device, and the battery comprises at least one positive electrode and one negative electrode.

2. The method according to claim 1, wherein the battery comprises a lithium ion battery.

3. The method according to claim 1, wherein the model coefficient comprises a coefficient of ion diffusion within at least one of the electrodes of the battery.

4. The method according to claim 1, wherein the battery state of charge estimator comprises a multi-layer model of the ionic behavior within at least one of the electrodes.

5. The method according to claim 4, wherein the electrode comprises the positive electrode of the battery.

6. The method according to claim 1,

wherein the model coefficient updater updates a model coefficient for a multi-layer model for an electrode of interest by

a) acquiring a measured cell terminal voltage value from a cell terminal voltage sensor;

b) acquiring an estimated cell terminal voltage value from the battery state of charge estimator;

c) determining the change increment for the measured cell terminal voltage;

d) determining the change increment for the estimated cell terminal voltage;

e) comparing the absolute value of the battery current to a predetermined value, and if the absolute value of the battery current is greater than the predetermined value then

f) comparing the absolute value of change increment for the measured cell terminal voltage to the absolute value of the change increment for the estimated cell terminal voltage;

g) updating the model coefficient, and

repeating steps a) through g) until either the absolute value of the change increment for the measured cell terminal voltage is equal to the absolute value of the change increment for the estimated cell terminal voltage, or the absolute value of the battery current is less than the predetermined value.

7. The method according to claim 6,

wherein when the absolute value of the change increment for the measured cell terminal voltage is greater than the absolute value of the change increment for the estimated cell terminal voltage, then

the updated model coefficient comprises the initial model coefficient minus an incremental value.

8. The method according to claim 6,

wherein when the absolute value of the change increment for the measured cell terminal voltage is less than the absolute value of the change increment for the estimated cell terminal voltage, then

the updated model coefficient comprises the initial model coefficient plus an incremental value.

9. The method according to claim 6, wherein the model coefficient comprises a coefficient of ion diffusion within at least one of the electrodes of the battery.

10. The method according to claim 1,

wherein the battery state of charge estimator determines the estimated state of charge of the battery and the estimated cell terminal voltage of the battery by:

acquiring the measured cell terminal voltage, the cell internal resistance, and the measured battery current;

using a multi-layer model to determine the state of charge for the multiple layers of the electrode of interest, and then

determining the estimated cell terminal voltage and the estimated state of charge of the battery.

11. The method according to claim 10, wherein the multi-layer model comprises

dividing each electrode into N layers of active electrode material;

acquiring the measured battery current;

acquiring the internal resistance of the battery;

determining the open circuit voltage of the battery, and

determining the state of charge for each of the layers of the active electrode material; and

wherein the state of charge of each of the layers of the active electrode material comprises a function of the difference between the respective states of charge of a layer'"'"'s adjacent layers, and

wherein N is a whole number greater than 1.

12. The method according to claim 10, wherein the electrode of interest comprises the positive electrode.

13. The method according to claim 11, wherein the state of charge of the first layer of active electrode material comprises the solution to the following equation:

14. The method according to claim 11, wherein the state of charge for each of the 2nd through the N−

1^th layer of active electrode material comprises the respective solutions to the following equation;

SOC_i+1(k+1)=SOC_i+1(k)+D_diff(SOC_i(k)−

SOC_i+1(k))−

D_diff(SOC_i+1(k)−

SOC_i+2(k))

for i=1 to N.

15. The method according to claim 11, wherein the state of charge of the N^th layer of active electrode material comprises the solution to the following equation:

SOC_N(k+1)=SOC_N(k)+D_diff(SOC<sub>N−

1</sub>(k)−

SOC_N(k)).

16. A battery management device comprising:

a battery having positive and negative electrodes;

a battery charger for charging the battery; and

a charging control system comprising a machine implemented method for determining the state of charge of either electrode of the battery, the machine implemented method comprising;

providing a measured cell terminal voltage and an estimated cell terminal voltage to a model coefficient updater;

updating a model coefficient by utilizing the measured cell terminal voltage and estimated cell terminal voltage;

providing battery current information and measured cell terminal voltage to a battery state of charge estimator;

providing the updated model coefficient to the battery state of charge estimator; and

determining an estimated state of charge of the battery and an estimated cell terminal voltage by utilizing the updated model coefficient, the battery current information, and the measured cell terminal voltage,

wherein the method is implemented on a processing device.

17. A battery management device according to claim 16, wherein the battery charger for charging the battery comprises a generator.

18. A battery management device according to claim 16, wherein the battery charger for charging the battery comprises a generator for a hybrid electric vehicle.

19. A battery management device according to claim 16, wherein the battery comprises a lithium ion battery.

20. A battery management device according to claim 16, wherein the processing device comprises a processor, a random access memory, a read only memory, an input device, an output device, and a bus,

wherein the bus connects the processor to one of more of the random access memory, the read only memory, the input device, and the output device, and

the processor comprises a central processing unit.