Method and apparatus for estimating state of battery

Patent metadata

Field	Value
Publication number	US-10422837-B2
Application number	US-201615007861-A
Country	US
Kind code	B2
Filing date	Jan 27, 2016
Priority date	Jun 11, 2015
Publication date	Sep 24, 2019
Grant date	Sep 24, 2019

Abstract

An apparatus for estimating a state of a battery including a data receiver configured to receive module data associated with a battery module included in a battery pack from the battery module, and a processor configured to acquire cell data corresponding to a battery cell included in the battery module from the module data, and determine a module state of the battery module based on the cell data.

First claim

What is claimed is:

1. An apparatus for estimating a pack state of a battery pack, the apparatus comprising:
a data receiver configured to receive, from a first battery module included in the battery pack and comprising battery cells connected in parallel with each other, first module data associated with the first battery module; and
a processor configured to
calculate, from the first module data, first cell data of a first battery cell of the battery cells of the first battery module based on a number of the battery cells connected in parallel with each other;
convert the first battery module to a first virtual cell by scaling, based on the first cell data, the first module data to data of a first single battery cell to generate first virtual cell data of the first virtual cell;
input, to a battery degradation model modeling degradation of the single battery cell through machine learning, the first virtual cell data;
output, from the battery degradation model, a first module state of the first battery module; and
determine, based on the output first module state, the pack state of the battery pack.

2. The apparatus of claim 1 , wherein processor is further configured to:
calculate, based on the output first module state, a first statistical value of the first module state; and
determine the pack state based on the first statistical value.

3. The apparatus of claim 1 , further comprising a data transmitter configured to transmit either one or both of the first module state and the pack state to an external device.

4. The apparatus of claim 1 , wherein:
the first module data comprises a value of a voltage signal output from the first battery module, a value of a current signal output from the first battery module, and a value of a temperature signal output from the first battery module; and
the scaling of the first module data to the data of the first single battery cell comprises calculating the data of the first single battery cell by assigning, as the data of the first single battery cell, the value of the voltage signal, the value of the temperature signal, and a current value calculated by dividing the value of the current signal by the number of the battery cells connected in parallel.

5. The apparatus of claim 1 , further comprising a preprocessor configured to preprocess the received first module data, and provide the preprocessed first module data to the processor.

6. The apparatus of claim 1 , wherein the first module data comprises any one or any combination of any two or more of a voltage signal output from the first battery module, a current signal output from the first battery module, and a temperature signal output from the first battery module.

7. The apparatus of claim 1 , wherein the processor is further configured to estimate a battery life of the battery pack based on the output first module state, and to determine an amount of charge of the battery pack based on the estimated battery life.

8. The apparatus of claim 1 , wherein the battery pack comprises a plurality of battery modules connected in series with each other; and
each of the plurality of battery modules comprises a respective plurality of battery cells connected in parallel with each other.

9. The apparatus of claim 1 , wherein the battery degradation model comprises a data-driven model comprising a neural network, or an equivalent circuit model expressing a relationship between the cell data and the pack state.

10. The apparatus of claim 1 , wherein the battery degradation model is a machine learning model comprising a neural network.

11. The apparatus of claim 10 , wherein the neural network is trained using a plurality of pattern profiles determined from simulations of a battery.

12. The apparatus of claim 1 , wherein the battery pack further comprises a second battery module connected in series to the first battery module, and wherein the processor is further configured to:
receive, from the second battery module, second module data associated with the second battery module; and
calculate, from the second module data, second cell data of a second battery cell of a plurality of battery cells of the second battery module;
convert the second battery module to a second virtual cell by scaling, based on the second cell data, the second module data to data of a second single battery cell to generate second virtual cell data of the second virtual cell;
input, to the battery degradation model, the second virtual cell data;
output, from the battery degradation model, a second module state of the second battery module; and
determine, based on the first module state and the output second module state, the pack state of the battery pack.

13. The apparatus of claim 12 , wherein the pack state of the battery pack comprises one of a minimum of the first module state and the second module state, a maximum of the first module state and the second module state, or a median of the first module state and the second module state.

14. The apparatus of claim 13 , wherein the battery pack is mounted in an electric vehicle (EV); and
the processor is further configured to select the minimum of the first module state and the second module state, and determine the pack state based on the minimum.

15. A method of estimating a pack state of a battery pack, the method comprising:
receiving, from a first battery module included in the battery pack and comprising battery cells connected in parallel with each other, first module data;
calculating, from the first module data, first cell data of a first battery cell of the battery cells of the first battery module based on a number of the battery cells connected in parallel with each other;
converting the first battery module to a first virtual cell by scaling, based on the first cell data, the first module data to data of a first single battery cell to generate first virtual cell data of the first virtual cell;
inputting, to a battery degradation model modeling degradation of the single battery cell through machine learning, the scaled first virtual cell data;
outputting, from the battery degradation model, a first module state of the first battery module; and
determining, based on the output first module state, the pack state of the battery pack.

16. The method of claim 15 , wherein the determining of the pack state comprises:
calculating, based on the output first module state, a first statistical value of the first module state; and
determining the pack state based on the first statistical value.

17. A non-transitory computer-readable storage medium storing instructions to cause computing hardware to perform the method of claim 15 .

18. An apparatus for estimating a pack state of a battery pack having an nSmP structure, n and m being integers equal to or greater than “1,” the apparatus comprising:
a data receiver configured to receive respective module data from each of a plurality of battery modules in the battery pack, wherein each battery module of the plurality of battery modules comprises respective battery cells connected in parallel with each other and has a 1SmP structure; and
a processor configured to
calculate, for each battery module, respective cell data corresponding to each of the respective battery cells of each battery module based on the respective module data and a number of the respective battery cells connected in parallel with each other;
convert each battery module to a respective virtual cell by scaling, based on the respective cell data of the battery module, the respective module data of ea

Assignees

• Samsung Electronics Co Ltd

Inventors

• Park Sangdo
• You Kaeweon

Classifications

• G01R31/3648 Primary

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

• G01R31/367

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

• G01R31/392

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

• G01R31/396 Primary

  Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery · CPC title