# US 12,591,020 B2 · Determination device, deterioration determination system, work support device, deterioration determination method, and computer program

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- Number: US 12,591,020 B2
- Document ID: US12591020B2
- Status: Granted
- Kind: B2
- Application number: 17/794,179
- Filed: 2021-01-19
- Published: 2023-02-16
- Granted: 2026-03-31
- Earliest claimed: 2020-01-24
- Assignees: GS Yuasa International Ltd.
- Applicants: Gs Yuasa International Ltd.
- Inventors: Takao Ohmae; Tomohiro Uchiyama
- Web page: https://patentgrep.com/patent/US12591020B2

## CPC

- G01R 31/392: Determining battery ageing or deterioration, e.g. state of health
- H01M 10/42: Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells (H01M10/60 takes precedence)
- G01R 31/367: Software therefor, e.g. for battery testing using modelling or look-up tables
- G01R 31/396: Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
- G06Q 10/063: Operations research, analysis or management
- G06Q 10/20: Administration of product repair or maintenance
- G06Q 10/30: Administration of product recycling or disposal
- G06Q 30/01: Customer relationship services
- G06Q 50/06: Energy or water supply
- G06Q 50/40: Business processes related to the transportation industry (shipping G06Q10/083)
- H01M 10/06: Lead-acid accumulators (semi-lead accumulators H01M10/20)
- H01M 10/4285: {Testing apparatus}
- H01M 10/48: 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)
- H01M 10/484: {for measuring electrolyte level, electrolyte density or electrolyte conductivity}
- Y02E 60/10: Energy storage using batteries
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- H01M: PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION

## Abstract

A determination device (1) includes: an acquisition unit that acquires determination information for determining a degree of deterioration or guarantee of a lead-acid battery (3); a determination unit that determines the degree of deterioration or guarantee of the lead-acid battery (3) by referring to a database (142) that stores the determination information and the degree of deterioration or guarantee of the lead-acid battery (3) in association with each other based on the acquired determination information; and an output unit that outputs a result determined by the determination unit.

## Claims

### Claim 1

A determination device comprising:
a processor and a storage,
wherein the processor is configured to:
acquire training data in which determination information is associated with a degree of deterioration or a degree of guarantee of a lead-acid battery, the determination information including at least one of appearance information or image information of the lead-acid battery;
train a multilayer neural network by:
inputting, in an input layer of the multilayer neural network, the determination information in the training data;
outputting, from an output layer of the multilayer neural network, a determination result of a degree of deterioration or a degree of guarantee;
comparing the determination result output from the output layer with a correct value which is information labeled with the determination information in the training data; and
optimizing a parameter used for a calculation processing in an intermediate layer of the multilayer neural network so that an output value from the output layer approaches the correct value using an error back propagation method, the parameter being at least one of a coupling coefficient or a coefficient of an activation function;
store the trained multilayer neural network in the storage;
receive the determination information from a terminal connected to the determination device via a network;
input the received determination information to the trained multilayer neural network;
acquire the degree of deterioration or the degree of guarantee of the lead-acid battery from the trained multilayer neural network; and
retrain the trained multilayer neural network by:
comparing the degree of deterioration or the degree of guarantee determined using the trained multilayer neural network and the degree of deterioration or the degree of guarantee obtained by actual measurement;
if the degree of deterioration or the degree of guarantee determined using the trained multilayer neural network matches the degree of deterioration or the degree of guarantee obtained by actual measurement, inputting pieces of training data in which the determination result is associated with the degree of deterioration or the degree of guarantee determined using the trained multilayer neural network in the trained multilayer neural network; and
if the degree of deterioration or the degree of guarantee determined using the trained multilayer neural network does not match the degree of deterioration or the degree of guarantee obtained by actual measurement, inputting training data in which the determination result is associated with the degree of deterioration or the degree of guarantee obtained by actual measurement in the trained multilayer neural network,
wherein the degree of deterioration includes information indicating that a deterioration is caused by a user's fault,
the degree of guarantee includes:
a first degree indicating that the lead-acid battery is returned to a user as a normal product;
a second degree indicating that although the lead-acid battery is normal, the lead-acid battery is returned after performing auxiliary charge;
a third degree indicating that the lead-acid battery is exchanged as an abnormal product; and
a fourth degree indicating that although the lead-acid battery is an abnormal product, an abnormality has occurred due to an action of the user, and replacement is performed for a fee, and
the multilayer neural network is configured to input the image information to a plurality of convolution layers and pooling layers to compress the image information, combine data of the image information of the pooling layers with other determination information, and then further input the image information to the plurality of convolution layers and pooling layers to compress the combined data of the image information and other determination information, and output the degree of deterioration or the degree of guarantee.

### Claim 2

A deterioration determination method by a determination device comprising a storage and a processor, the deterioration determination method comprising:
acquiring, by the processor, training data in which determination information is associated with a degree of deterioration or a degree of guarantee of a lead-acid battery, the determination information including at least one of appearance information or image information of the lead-acid battery;
training, by the processor, a multilayer neural network by:
inputting, in an input layer of the multilayer neural network, the determination information in the training data;
outputting, from an output layer of the multilayer neural network, a determination result of a degree of deterioration or a degree of guarantee;
comparing the determination result output from the output layer with a correct value which is information labeled with the determination information in the training data; and
optimizing a parameter used for a calculation processing in an intermediate layer of the multilayer neural network so that an output value from the output layer approaches the correct value using an error back propagation method, the parameter being at least one of a coupling coefficient or a coefficient of an activation function;
storing, by the processor, the trained multilayer neural network in the storage;
receiving, by the processor, the determination information from a terminal connected to the determination device via a network;
inputting, by the processor, the received determination information to the trained multilayer neural network;
acquiring, by the processor, the degree of deterioration or the degree of guarantee of the lead-acid battery from the trained multilayer neural network; and
retraining the trained multilayer neural network by:
comparing the degree of deterioration or the degree of guarantee determined using the trained multilayer neural network and the degree of deterioration or the degree of guarantee obtained by actual measurement;
if the degree of deterioration or the degree of guarantee determined using the trained multilayer neural network matches the degree of deterioration or the degree of guarantee obtained by actual measurement, inputting pieces of training data in which the determination result is associated with the degree of deterioration or the degree of guarantee determined using the trained multilayer neural network in the trained multilayer neural network; and
if the degree of deterioration or the degree of guarantee determined using the trained multilayer neural network does not match the degree of deterioration or the degree of guarantee obtained by actual measurement, inputting training data in which the determination result is associated with the degree of deterioration or the degree of guarantee obtained by actual measurement in the trained multilayer neural network
wherein the degree of deterioration includes information indicating that a deterioration is caused by a user's fault,
the degree of guarantee includes:
a first degree indicating that the lead-acid battery is returned to a user as a normal product;
a second degree indicating that although the lead-acid battery is normal, the lead-acid battery is returned after performing auxiliary charge;
a third degree indicating that the lead-acid battery is exchanged as an abnormal product; and
a fourth degree indicating that although the lead-acid battery is an abnormal product, an abnormality has occurred due to an action of the user, and replacement is performed for a fee, and
the multilayer neural network is configured to input the image information to a plurality of convolution layers and pooling layers to compress the image information, combine data of the image information of the pooling layers with other determination information, and then further input the image information to the plurality of convolution layers and pooling layers to compress the combined data of the image information and other determination information, and output the degree of deterioration or the degree of guarantee.