SYSTEM AND METHOD FOR ESTIMATING LONG TERM CHARACTERISTICS OF BATTERY

• US 20110191278A1
• Filed: 08/21/2008
• Published: 08/04/2011
• Est. Priority Date: 08/23/2007
• Status: Active Grant

• Current Assignee: LG Energy Solution Limited (LG Corporation)
• Original Assignee: LG Chem Limited (LG Corporation)
• Inventors: Choo, Yeon-Uk, Cho, Jeong-ju, Song, Hyun-Kon, Lee, Ho-Chun, Son, Mi-Young
• Granted Patent: US 9,255,973 B2
• Time in Patent Office: Days
• US Class Current: 706/17
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G06N 7/00 Computing arrangements based on specific mathematical models

Abstract

A system for estimating long term characteristics of a battery includes a learning data input unit for receiving initial characteristic learning data and long term characteristic learning data of a battery to be a learning object; a measurement data input unit for receiving initial characteristic measurement data of a battery to be an object for estimation of long term characteristics; and an artificial neural network operation unit for receiving the initial characteristic learning data and the long term characteristic learning data from the learning data input unit to allow learning of an artificial neural network, receiving the initial characteristic measurement data from the measurement data input unit and applying the learned artificial neural network thereto, and thus calculating long term characteristic estimation data from the initial characteristic measurement data of the battery and outputting the long term characteristic estimation data.

Claims

1. A system for estimating long term characteristics of a battery, comprising:

a learning data input unit for receiving initial characteristic learning data and long term characteristic learning data of a battery to be a learning object;

a measurement data input unit for receiving initial characteristic measurement data of a battery to be an object for estimation of long term characteristics; and

an artificial neural network operation unit for receiving the initial characteristic learning data and the long term characteristic learning data from the learning data input unit to allow learning of an artificial neural network, receiving the initial characteristic measurement data from the measurement data input unit and applying the learned artificial neural network thereto, and thus calculating long term characteristic estimation data from the initial characteristic measurement data of the battery and outputting the long term characteristic estimation data.

2. The system according to claim 1,

wherein the learned artificial neural network has at least one neuron layer arranged in series, and

wherein the neuron layer converts an input vector into an output vector such that a bias vector and a weight matrix calculated by the learning of the artificial neural network are reflected on the input vector, the input vector on which the bias vector and the weight matrix are reflected is processed by a neuron transfer function, and then result of the neuron transfer function is output as an output vector.

3. The system according to claim 2,

wherein, in the serial arrangement of the neuron layer, the first neuron layer has an input vector composed of the initial characteristic measurement data.

4. The system according to claim 1,

wherein the data relating to initial characteristics includes a charging characteristic variation data of a battery, measured in a battery activating process;

or a charging characteristic variation data, a discharging characteristic variation data, a thickness variation data, or an open circuit voltage variation data of a battery, obtained by measurement of initial cycle characteristics, and

wherein the data relating to long term characteristics includes a charging characteristic variation data, a discharging characteristic variation data, a thickness variation data, or an open circuit voltage variation data of a battery at predetermined long term cycles.

5. The system according to claim 1, further comprising an initial characteristic measurement sensor for measuring a charging characteristic of a battery put into an activating process and then outputting the measured charging characteristic as an initial characteristic measurement data,

wherein the measurement data input unit receives the initial characteristic measurement data from the initial characteristic measurement sensor.

6. The system according to claim 1, further comprising a display for receiving the long term characteristic estimation data from the artificial neural network operation unit to display the long term characteristic estimation data in a graphic-user interface through a display device.

7. The system according to claim 1, further comprising a long term characteristic evaluation unit for receiving the long term characteristic estimation data from the artificial neural network operation unit and determining long term characteristic quality by comparing the received long term characteristic estimation data with a criterion long term characteristic data.

8. The system according to claim 7,

wherein the long term characteristic evaluation unit outputs a long term characteristic quality determination result of the battery in a graphic-user interface through a display device.

9. A method for estimating long term characteristics of a battery, comprising:

(a) receiving initial characteristic learning data and long term characteristic learning data of a battery to be a learning object;

(b) conducting artificial neural network learning for the received initial characteristic learning data and the received long term characteristic learning data;

(c) receiving initial characteristic measurement data of a battery to be an object for estimation of long term characteristics; and

(d) calculating long term characteristic estimation data from the initial characteristic measurement data of the battery by applying the learned artificial neural network thereto, and then outputting the long term characteristic estimation data.

10. The method according to claim 9,

wherein the learned artificial neural network has at least one neuron layer arranged in series, and

wherein the step (d) includes:

(d1) converting the initial characteristic measurement data into an input vector;

(d2) inputting the converted input vector into a first neuron layer of the neuron layer arrangement;

(d3) each neuron layer of the neuron layer arrangement reflecting a bias vector and a weight matrix calculated by the learning of the artificial neural network on the input vector and then processing the input vector by a neuron transfer function such that the input vector is converted into an output vector and then output; and

(d4) a last neuron layer of the neuron layer arrangement outputting a long term characteristic estimation vector as an output vector.

11. The method according to claim 9,

wherein the data relating to initial characteristics includes a charging characteristic variation data of a battery, measured in a battery activating process;

or a charging characteristic variation data, a discharging characteristic variation data, a thickness variation data, or an open circuit voltage variation data of a battery, obtained by measurement of initial cycle characteristics, and

wherein the data relating to long term characteristics includes a charging characteristic variation data, a discharging characteristic variation data, a thickness variation data, or an open circuit voltage variation data of a battery at predetermined long term cycles.

12. The method according to claim 9,

wherein the initial characteristic learning data is a charging characteristic variation data of the battery to be a learning object, put into a battery activating process, and the long term characteristic learning data is a charging characteristic variation data, a discharging characteristic variation data, a thickness variation data, or an open circuit voltage variation data of the battery to be a learning object at predetermined long term cycles, and

wherein the step (c) includes:

putting the battery to be an object for long term characteristic estimation into the battery activating process;

measuring a charging characteristic variation from the battery; and

receiving the measured charging characteristic variation data as an initial characteristic measurement data.

13. The method according to claim 9, further comprising:

visually displaying the long term characteristic estimation data.

14. The method according to claim 9, further comprising:

comparing the long term characteristic estimation data with a criterion long term characteristic data to determine long term characteristic quality of the battery.

15. The method according to claim 14, further comprising:

visually displaying a long term characteristic quality determination result of the battery.