Battery state estimation method and apparatus

Patent metadata

Field	Value
Publication number	US-2021190866-A1
Application number	US-202016886967-A
Country	US
Kind code	A1
Filing date	May 29, 2020
Priority date	Dec 23, 2019
Publication date	Jun 24, 2021
Grant date	—

Abstract

A battery state estimation method includes acquiring an initial value of a vector-type parameter for modeling an electrochemical-thermal (ECT) model of a battery, extracting a predetermined point from the vector-type parameter based on the initial value, generating a target parameter based on the predetermined point, to minimize an error between an actual state of the battery and a state of the battery acquired from the ECT model, and estimating the state of the battery based on the target parameter.

First claim

What is claimed is:

1 . A battery state estimation method comprising:
acquiring an initial value of a vector-type parameter for modeling an electrochemical-thermal (ECT) model of a battery; extracting a predetermined point from the vector-type parameter based on the initial value; generating a target parameter based on the predetermined point, the target parameter minimizing an error between an actual state of the battery and a state of the battery acquired from the ECT model; and estimating the state of the battery based on the target parameter.

2 . The battery state estimation method of claim 1 , wherein a dimension of the vector-type parameter is based on a stoichiometry of an electrode during charging and discharging of the battery.

3 . The battery state estimation method of claim 1 , wherein the extracting of the predetermined point comprises extracting the predetermined point based on a value obtained by differentiating the vector-type parameter at least once with respect to a stoichiometry.

4 . The battery state estimation method of claim 3 , wherein the extracting of the predetermined point based on the value obtained by differentiating the vector-type parameter at least once with respect to the stoichiometry comprises extracting a point at which the value obtained by differentiating the vector-type parameter at least once is a predetermined value.

5 . The battery state estimation method of claim 4 , wherein the predetermined value is zero.

6 . The battery state estimation method of claim 1 , wherein the generating of the target parameter comprises:
setting a search boundary of a component of the vector-type parameter; and generating the target parameter based on the search boundary.

7 . The battery state estimation method of claim 6 , wherein the setting of the search boundary comprises setting the search boundary based on a gradient for a stoichiometry of the component of the vector-type parameter.

8 . The battery state estimation method of claim 1 , wherein the generating of the target parameter comprises:
acquiring the state of the battery from the ECT model using a parameter corresponding to the predetermined point; calculating the error between the actual state of the battery and the state of the battery acquired from the ECT model; and generating the target parameter to minimize the error.

9 . The battery state estimation method of claim 8 , wherein the generating of the target parameter to minimize the error comprises:
generating a set of parameters for the ECT model; generating a candidate parameter based on the set of the parameters; and determining the candidate parameter as the target parameter, in response to an error calculated based on the candidate parameter being minimized.

10 . The battery state estimation method of claim 1 , wherein the error comprises a sum of squared errors (SSE) between the actual state of the battery and the state of the battery acquired from the ECT model, for each of a plurality of points in time.

11 . The battery state estimation method of claim 1 , wherein the estimating of the state of the battery comprises:
interpolating a parameter corresponding to each of points other than the predetermined point based on the target parameter; and estimating the state of the battery based on the interpolated parameter.

12 . The battery state estimation method of claim 1 , wherein the state of the battery comprises a voltage of the battery with respect to a current and a temperature.

13 . A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the operating method of claim 1 .

14 . A battery state estimation apparatus comprising:
a processor configured to:
acquire an initial value of a vector-type parameter for modeling of an electrochemical-thermal (ECT) model of a battery;
extract a predetermined point from the vector-type parameter based on the initial value;
generate a target parameter based on the predetermined point, the target parameter minimizing an error between an actual state of the battery and a state of the battery acquired from the ECT model; and
estimate the state of the battery based on the target parameter.

15 . The battery state estimation apparatus of claim 14 , wherein a dimension of the vector-type parameter is based on a stoichiometry of an electrode during charging and discharging of the battery.

16 . The battery state estimation apparatus of claim 14 , wherein the processor is further configured to extract the predetermined point based on a value obtained by differentiating the vector-type parameter at least once with respect to a stoichiometry.

17 . The battery state estimation apparatus of claim 16 , wherein the processor is further configured to extract a point at which the value obtained by differentiating the vector-type parameter at least once is a predetermined value.

18 . The battery state estimation apparatus of claim 17 , wherein the predetermined value is zero.

19 . The battery state estimation apparatus of claim 14 , wherein the processor is further configured to:
set a search boundary of a component of the vector-type parameter; and generate the target parameter based on the search boundary.

20 . The battery state estimation apparatus of claim 19 , wherein the processor is further configured to set the search boundary based on a gradient for a stoichiometry of the component of the vector-type parameter.

21 . The battery state estimation apparatus of claim 14 , wherein the processor is further configured to:
acquire the state of the battery from the ECT model using a parameter corresponding to the predetermined point; calculate the error between the actual state of the battery and the state of the battery acquired from the ECT model; and generate the target parameter to minimize the error.

22 . The battery state estimation apparatus of claim 21 , wherein the processor is further configured to:
generate a set of parameters for the ECT model; generate a candidate parameter based on the set of the parameters; and determine the candidate parameter as the target parameter, in response to an error calculated based on the candidate parameter being minimized.

23 . The battery state estimation apparatus of claim 14 , wherein the error comprises a sum of squared errors (SSE) between the actual state of the battery and the state of the battery acquired from the ECT model, for each of a plurality of points in time.

24 . The battery state estimation apparatus of claim 14 , wherein the processor is further configured to:
interpolate a parameter corresponding to each of points other than the predetermined point based on the target parameter; and estimate the state of the battery based on the interpolated parameter.

25 . The battery state estimation apparatus of claim 14 , wherein the state of the battery comprises a voltage of the battery with respect to a current and a temperature.

26 . The battery state estimation apparatus of claim 14 , further comprising a memory configured to store the electrochemical-thermal (ECT) model and instructions executed by the processor to configure the processor to acquire an initial value, to extract the predetermined point, to generate the target parameter, and to estimate the state of the battery.

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Assignees

• Samsung Electronics Co Ltd

Inventors

• You Kaeweon
• Park Jeonghyun
• Kim Jinho

Classifications

• G01R31/367 Primary

  Software therefor, e.g. for battery testing using modelling or look-up tables · CPC title

• G01R31/3648

  comprising digital calculation means, e.g. for performing an algorithm · CPC title

• G01R31/392

  Determining battery ageing or deterioration, e.g. state of health · CPC title

• G01R31/3842

  combining voltage and current measurements · CPC title