Apparatus and method for estimating state of battery

Patent metadata

Field	Value
Publication number	US-10935608-B2
Application number	US-201715405650-A
Country	US
Kind code	B2
Filing date	Jan 13, 2017
Priority date	Jan 14, 2016
Publication date	Mar 2, 2021
Grant date	Mar 2, 2021

Abstract

Apparatus and method for estimating a state of a battery is provided. According to one aspect, a battery state estimation apparatus includes a state of health (SOH) estimator configured to estimate SOH of a battery based on degradation of the battery and the data acquired from the battery, and a state of charge (SOC) estimator configured to estimate the SOC of the battery based on the SOH of the battery.

First claim

What is claimed is:

1. An apparatus to estimate a state of a battery, the apparatus comprising:
one or more sensors configured to measure data over time including any one or any combination of a voltage, a current, a temperature, a current rate, and a charge/discharge cycle of the battery; and
a processor configured to:
estimate a state of health (SOH) of the battery over time by inputting the measured data to an input layer of a pre-learned neural network and estimating the SOH based on an output of an output layer of the neural network;
in response to an end of a parameter update cycle being reached, update one or more electrode parameters of the battery based on the estimated SOH; and
estimate a state of charge (SOC) of the battery by applying the updated one or more electrode parameters to an electrochemical model of the battery.

2. The apparatus of claim 1 , wherein the processor comprises:
a state of health (SOH) estimator configured to perform the estimation of the SOH; and
a state of charge (SOC) estimator configured to perform the estimation of the SOC.

3. The apparatus of claim 1 , further comprising a data collector configured to collect the data measured by the one or more sensors.

4. The apparatus of claim 1 , wherein the neural network is trained through deep learning to consider a degradation level of operation of the battery.

5. The apparatus of claim 1 , wherein the one or more electrode parameters are of the electrochemical model of the battery.

6. The apparatus of claim 5 , wherein the one or more electrode parameters comprises any one or any combination of an electrode volume ratio parameter, a film resistance parameter, and an electrode particle size parameter.

7. The apparatus of claim 5 , wherein the parameter update cycle is determined based on any one or any combination of a battery capacity, a battery operation time, a charge/discharge time of the battery, and a number of charge/discharge cycles of the battery.

8. The apparatus of claim 5 , wherein the performance of the estimation of the SOC of the battery using the electrochemical model with the updated one or more electrode parameters includes estimating a battery state comprising potential and density distribution.

9. A processor implemented method to estimate a state of a battery, the method comprising:
measuring, using one or more sensors, data over time including any one or any combination of a voltage, a current, a temperature, a current rate, and a charge/discharge cycle of the battery;
estimating a state of health (SOH) of the battery over time by inputting the measured data to an input layer of a pre-learned neural network and estimating the SOH based on an output of an output layer of the neural network;
in response to an end of a parameter update cycle being reached, updating one or more electrode parameters of the battery based on the estimated SOH; and
estimating a state of charge (SOC) of the battery by applying the updated one or more electrode parameters to an electrochemical model of the battery.

10. The method of claim 9 , further comprising:
collecting the data measured by the one or more sensors.

11. The method of claim 9 , wherein the neural network is trained through deep learning to consider a degradation level of operation of the battery.

12. The method of claim 9 , wherein the the parameter update cycle is determined based on any one or any combination of a battery capacity, a battery operation time, a charge/discharge time of the battery, and a number of charge/discharge cycles of the battery.

13. The method of claim 9 , wherein the one or more electrode parameters are of the electrochemical model of the battery.

14. The method of claim 13 , wherein the one or more electrode parameters comprises any one or any combination of an electrode volume ratio parameter, a film resistance parameter, and an electrode particle size parameter.

15. The method of claim 13 , wherein the performance of the estimation of the SOC of the battery using the electrochemical model with the updated one or more electrode parameters includes estimating a battery state comprising potential and density distribution.

16. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 9 .

17. The method of claim 9 , wherein the estimating of the SOC comprises, in response to the one or more electrode parameters being updated, applying the updated one or more electrode parameters to the electrochemical model to estimate the SOC of the battery.

18. A processor implemented method to estimate a state of a battery, the method comprising:
measuring, using one or more sensors, data including any one or any combination of a voltage, a current, a temperature, a current rate, and a charge/discharge cycle of the battery;
inputting the measured data into an input layer of a pre-learned neural network;
estimating a state of health (SOH) of the battery based on an output of an output layer of the neural network; and
in response to an end of a parameter update cycle being reached, updating one or more electrode parameters of an electrochemical model and estimating a state of charge (SOC) of the battery using the electrochemical model with the updated one or more electrode parameters.

Assignees

• Samsung Electronics Co Ltd

Inventors

• Song Tae Won
• Park Sang Do
• Yeo Tae Jung
• Kim Jin Ho

Classifications

• H01M10/48

  Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte (constructional details of current conducting connections for detecting conditions inside cells or batteries, e.g. details of voltage sensing terminals, H01M50/569) · CPC title

• G01R31/392 Primary

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

• Y02E60/10

  Energy storage using batteries · CPC title

• G01R31/382

  Arrangements for monitoring battery or accumulator variables, e.g. SoC · CPC title

• G01R31/367

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