IP Library › Granted Patent US 11,480,621
Granted Patent B2
US 11,480,621 · App. 16/753,839 · Granted Oct 25, 2022

Capacity estimation method and capacity estimation system for power storage device

Inventors: Ryota Tajima (Kanagawa, JP); Toshiyuki Isa (Kanagawa, JP); Akihiro Chida (Kanagawa, JP)
Assignee: SEMICONDUCTOR ENERGY LABORATORY CO., LTD.
G01R31/387H02J9/06
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 11,480,621
App. No.
16/753,839
Granted
Oct 25, 2022
Kind
B2
Abstract

It is difficult to know the remaining amount and the degradation state of a power storage device, and it is also difficult to estimate how long the power storage device can be used. Data obtained through midway discharge and mid-to-full charge is used as the learning data to calculate the degradation state and the capacity. In other words, the learning data includes both a discharge curve of midway discharge and a charge curve of mid-to-full charge, and neural network processing is performed with the use of the learned data.

Claims (30)

1. A capacity estimation method for a power storage device comprising a plurality of lithium-ion secondary batteries, comprising:

performing self-discharging of a secondary battery with an SOC of 100% over time during a first period;

charging the secondary battery from an SOC of a self-discharged state to a fully charged state during a second period;

discharging the secondary battery to SOC 80% using a discharging circuit during a third period;

charging the secondary battery to a fully charged state using a charging circuit during a fourth period;

obtaining a first charge-discharge data during the third period and the fourth period; and

calculating a degradation state from the first charge-discharge data by neural network processing.

2. The capacity estimation method according to claim 1 ,

wherein the power storage device is installed in an uninterruptible power supply system, and

wherein the charge from SOC 80% to SOC 100% is performed by constant voltage charging.

3. The capacity estimation method according to claim 1 , wherein the power storage device is a lithium-ion secondary battery installed inside an uninterruptible power supply.

4. The capacity estimation method according to claim 1 , wherein the power storage device is a lithium-ion secondary battery installed inside a vehicle.

5. A capacity estimation system for a power storage device comprising a plurality of lithium-ion secondary batteries connected in series, comprising:

performing self-discharging of a secondary battery with an SOC of 100% with a constant load during a first period;

charging the secondary battery from an SOC of a self-discharged state to a fully charged state during a second period;

discharging the secondary battery to SOC 80% using a discharging circuit during a third period;

charging the secondary battery to a fully charged state using a charging circuit during a fourth period to obtain a first charge data; and

calculating a degradation state from the first charge data by neural network processing.

6. The capacity estimation system according to claim 5 , wherein the power storage device is a lithium-ion secondary battery installed inside an uninterruptible power supply.

7. The capacity estimation system according to claim 5 , wherein the power storage device is a lithium-ion secondary battery installed inside a vehicle.

8. A capacity estimation method for a power storage device, comprising:

preparing a first secondary battery as a test cell;

performing self-discharging of the first secondary battery with an SOC of 100% with a constant load during a first period;

charging the first secondary battery from an SOC of a self-discharged state to a fully charged state during a second period;

discharging the first secondary battery to SOC 80% during a third period;

charging the first secondary battery to a fully charged state during a fourth period;

obtaining learning data comprising the charge data during the fourth period;

learning the learning data in a neural network portion;

charging a second secondary battery from SOC 80% to a fully charged state; and

calculating a degradation state of the second secondary battery by the neural network portion after learning.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 6, 2020
From: TAJIMA, RYOTA; ISA, TOSHIYUKI; CHIDA, AKIHIRO
To: SEMICONDUCTOR ENERGY LABORATORY CO., LTD.
Reel/Frame 052316/0813 →
Priority Claims (1)
JP JP2017-213051 · Nov 2, 2017 · national
Continuity (1)
Related Publication 20200292624A1 · Sep 17, 2020
Cited By (2)
US 12,537,241 US 12,545,145