Granted Patent B2

US 12,618,913 · App. 17/689,359 · Granted May 5, 2026

Inventors: Daebong Jung (Hwaseong-si, KR); Young Hun Sung (Hwaseong-si, KR)

Assignee: SAMSUNG ELECTRONICS CO., LTD.

G01R31/387H01M10/4285H01M10/443

Abstract

Claims (78)

1 . A device with battery model optimization, the device comprising:

a processor configured to:

perform optimization on a battery model, for determining optimized parameter values of parameters of the battery model,

wherein, to perform the optimization, the processor is configured to:

select target parameters from among the parameters of the battery model;

set a current boundary condition for each of the target parameters;

determine an optimized parameter value of each of the target parameters based on the set current boundary condition;

set a subsequent boundary condition reduced from the current boundary condition based on the determined optimized parameter value; and

determine a subsequent optimized parameter value of each of the target parameters based on the subsequent boundary condition; and

estimate a state of a battery using the battery model having the optimized parameter values of the parameters of the battery model,

wherein, for the performing optimization, the processor is further configured to perform the optimization for each of state of charge (SOC) intervals by determining the SOC intervals as corresponding to respective degrees of progress in charging or discharging the battery,

wherein the processor is configured to

determine whether a number of performances of the optimization reaches a preset number of performances, and

until the number of performances of the optimization reaches the preset number and until an optimization loss satisfies a defined condition, iteratively perform the setting of the subsequent boundary condition reduced from the current boundary condition and the determining of the subsequent optimized parameter value of each of the target parameters based on the subsequent boundary condition, and

wherein, for the setting of the subsequent boundary condition, the processor is configured to:

determine a target change direction of a diffusion parameter based on a voltage error between a voltage of the battery that is estimated through the battery model and a voltage of the battery that is based on profile data of the battery; and

set the subsequent boundary condition based on the determined target change direction.

2 . The device of claim 1 , wherein the processor is configured to perform the optimization for each of predefined temperature intervals.

3 . The device of claim 1 , wherein the processor is configured to select the target parameters based on a value obtained by performing differentiation one or more times on the parameters of the battery model.

4 . The device of claim 1 , wherein

for the setting of the subsequent boundary condition, the processor is configured to change the current boundary condition for all the target parameters based on the optimized parameter value retrieved based on the current boundary condition, and

the changed boundary condition corresponds to the subsequent boundary condition.

5 . The device of claim 1 , wherein the processor is configured to:

select points associated with a diffusion characteristic of the battery from among the parameters of the battery model; and

for the selecting of the target parameters, determine the target parameters based on the selected points.

6 . The device of claim 1 , wherein the processor is configured to:

determine an estimated state value of the battery model based on the target parameters;

determine an optimization loss based on a difference between the estimated state value and an actual state value obtained from profile data of the battery; and

adjust at least one of the target parameters such that the optimization loss is reduced.

7 . The device of claim 1 , wherein

the parameters of the battery model comprise a diffusion parameter dependent on an SOC level of the battery, and

the diffusion parameter comprises a charge parameter associated with charging of the battery and a discharge parameter associated with discharging of the battery.

8 . A processor-implemented method with battery model optimization, the method comprising:

selecting target parameters from among parameters of a battery model;

performing optimization on the target parameters, wherein the performing of the optimization comprises:

setting a current boundary condition for each of the target parameters;

determining an optimized parameter value of each of the target parameters based on the set current boundary condition;

setting a subsequent boundary condition reduced from the current boundary condition based on the determined optimized parameter value; and

determining a subsequent optimized parameter value of each of the target parameters based on the subsequent boundary condition; and

estimating a state of a battery using the battery model having the optimized parameter values of the parameters of the battery model,

wherein the performing optimization further includes performing the optimization for each of state of charge (SOC) intervals by determining the SOC intervals as corresponding to respective degrees of progress in charging or discharging the battery, and

wherein the performing optimization further includes determining whether a number of performances of the optimization reaches a preset number of performances, and until the number of performances of the optimization reaches the preset number and until an optimization loss satisfies a defined condition, iteratively performing the setting of the subsequent boundary condition reduced from the current boundary condition and the determining of the subsequent optimized parameter value of each of the target parameters based on the subsequent boundary condition, and

wherein the setting of the subsequent boundary condition comprises:

determining a target change direction of a diffusion parameter based on a voltage error between a voltage of the battery that is estimated through the battery model and a voltage of the battery that is based on profile data of the battery; and

setting the subsequent boundary condition based on the determined target change direction.

9 . The method of claim 8 , wherein the performing of the optimization comprises performing the optimization for each of predefined temperature intervals.

10 . A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the method of claim 8 .

11 . A battery power supplier, comprising:

a battery configured to supply power to an electronic device;

a battery model optimizing device configured to optimize a battery model corresponding to the battery, wherein, for the optimizing of the battery model, the battery model optimizing device is configured to:

select target parameters from among parameters of the battery model;

set a current boundary condition for each of the target parameters;

determine an optimized parameter value of each of the target parameters based on the set current boundary condition;

set a subsequent boundary condition reduced from the current boundary condition based on the determined optimized parameter value; and

determine a subsequent optimized parameter value of each of the target parameters based on the subsequent boundary condition; and

a processor configured to estimate a state of a battery using the battery model having the optimized parameter values of the parameters of the battery model,

wherein, for the performing optimization, the battery model optimizing device is further configured to perform the optimization for each of state of charge (SOC) intervals by determining the SOC intervals as corresponding to respective degrees of progress in charging or discharging the battery, and

wherein the processor is configured to determine whether a number of performances of the optimization reaches a preset number of performances, and

until the number of performances of the optimization reaches the preset number and until an optimization loss satisfies a defined condition, iteratively perform the setting of the subsequent boundary condition reduced from the current boundary condition and the determining of the subsequent optimized parameter value of each of the target parameters based on the subsequent boundary condition, and

wherein, for the setting of the subsequent boundary condition, the battery model optimizing device is configured to:

determine a target change direction of a diffusion parameter based on a voltage error between a voltage of the battery that is estimated through the battery model and a voltage of the battery that is based on profile data of the battery; and

set the subsequent boundary condition based on the determined target change direction.

12 . A processor-implemented method with battery model optimization, the method comprising:

setting a current boundary condition for a target parameter of a battery model;

determining an optimized parameter of the target parameter to be within the boundary condition;

setting a subsequent boundary condition, with a range reduced from the current boundary condition, based on a difference between a state of the battery estimated using the battery model with the optimized parameter and a predetermined state of the battery;

optimizing the battery model by determining a subsequent optimized parameter of the target parameter to be within the subsequent boundary condition, further including optimizing the battery model for each of state of charge (SOC) intervals by determining the SOC intervals as corresponding to respective degrees of progress in charging or discharging the battery; and

estimating a state of a battery using the battery model with optimized parameters of the battery model,

wherein the optimizing of the battery model further includes determining whether a number of performances of the optimization reaches a preset count of performances, and until the number of performances of the optimization reaches the preset count and until an optimization loss satisfies a defined condition, iteratively performing the setting of the subsequent boundary condition reduced from the current boundary condition and the determining of the subsequent optimized parameter value of the target parameter based on the subsequent boundary condition, and

wherein the setting of the subsequent boundary condition comprises:

determining a target change direction of a diffusion parameter based on a voltage error between a voltage of the battery that is estimated through the battery model and a voltage of the battery that is based on profile data of the battery; and

setting the subsequent boundary condition based on the determined target change direction.

13 . The method of claim 12 , wherein

the set current boundary condition comprises a lower limit and an upper limit, and

the determining of the optimized parameter comprises determining the optimized parameter to be greater than or equal to the lower limit and less than or equal to the upper limit.

14 . The method of claim 13 , wherein the setting of the subsequent boundary condition comprises, based on whether the state of the battery estimated using the battery model is greater than the predetermined state of the battery, either one of:

increasing at least one of the lower limit and the higher limit, and

decreasing at least one of the lower limit and the higher limit.

Assignments (1)

Priority Claims (1)

Continuity (1)

References Cited (31)