PL4055399T3 - Przewidywanie wydajności baterii - Google Patents
Przewidywanie wydajności bateriiInfo
- Publication number
- PL4055399T3 PL4055399T3 PL20799964.0T PL20799964T PL4055399T3 PL 4055399 T3 PL4055399 T3 PL 4055399T3 PL 20799964 T PL20799964 T PL 20799964T PL 4055399 T3 PL4055399 T3 PL 4055399T3
- Authority
- PL
- Poland
- Prior art keywords
- battery performance
- performance prediction
- prediction
- battery
- performance
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/003—Measuring mean values of current or voltage during a given time interval
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/3865—Arrangements for measuring battery or accumulator variables related to manufacture, e.g. testing after manufacture
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- H02J7/80—
-
- H02J7/82—
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Manufacturing & Machinery (AREA)
- Secondary Cells (AREA)
- Tests Of Electric Status Of Batteries (AREA)
- Power Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP19207777 | 2019-11-07 | ||
| PCT/EP2020/081300 WO2021089786A1 (en) | 2019-11-07 | 2020-11-06 | Battery performance prediction |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| PL4055399T3 true PL4055399T3 (pl) | 2025-06-23 |
Family
ID=68502957
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PL20799964.0T PL4055399T3 (pl) | 2019-11-07 | 2020-11-06 | Przewidywanie wydajności baterii |
Country Status (9)
| Country | Link |
|---|---|
| US (1) | US20220373601A1 (pl) |
| EP (1) | EP4055399B1 (pl) |
| JP (2) | JP7497432B2 (pl) |
| KR (1) | KR102902076B1 (pl) |
| CN (1) | CN114651183B (pl) |
| ES (1) | ES3025683T3 (pl) |
| HU (1) | HUE071081T2 (pl) |
| PL (1) | PL4055399T3 (pl) |
| WO (1) | WO2021089786A1 (pl) |
Families Citing this family (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115179812A (zh) * | 2021-03-18 | 2022-10-14 | 华为数字能源技术有限公司 | 基于云端协同的电池管理系统、车及电池管理方法 |
| WO2023283341A2 (en) * | 2021-07-08 | 2023-01-12 | The Regents Of The University Of Michigan | Early-life diagnostics for fast battery formation protocols and their impacts to long-term aging |
| EP4123321B1 (de) | 2021-07-23 | 2024-10-30 | Siemens Aktiengesellschaft | Verfahren, vorrichtung und computerprogrammprodukt zur restwertbestimmung von batteriespeichern |
| EP4123320B1 (de) * | 2021-07-23 | 2024-03-13 | Siemens Aktiengesellschaft | Verfahren zum bestimmen eines kapazitätsverlusts eines batteriespeichers, vorrichtung und computerprogrammprodukt |
| EP4123319B1 (de) | 2021-07-23 | 2024-02-14 | Siemens Aktiengesellschaft | Verfahren, vorrichtung und computerprogrammprodukt zur lebensdauerabschätzung von batteriespeichern |
| US12149110B2 (en) * | 2021-10-14 | 2024-11-19 | Arm Limited | Battery cell monitoring systems, battery packs, and methods of operation of the same |
| KR102872286B1 (ko) * | 2021-12-10 | 2025-10-16 | 주식회사 엘지에너지솔루션 | 배터리 수명 예측 장치 및 그것의 동작 방법 |
| CN114499408A (zh) * | 2022-02-16 | 2022-05-13 | 一道新能源科技(衢州)有限公司 | 基于电池大数据的信息测试方法及系统 |
| CN114966413B (zh) * | 2022-05-27 | 2023-03-24 | 深圳先进技术研究院 | 一种储能电池包的荷电状态预测方法 |
| KR20230167664A (ko) * | 2022-06-02 | 2023-12-11 | 주식회사 엘지에너지솔루션 | 배터리 셀 수명 진단 장치 및 그것의 동작 방법 |
| KR102714714B1 (ko) * | 2023-01-31 | 2024-10-11 | 국립한밭대학교 산학협력단 | 신경망 기반의 배터리 내부저항 예측 방법 및 장치 |
| CN116626508A (zh) * | 2023-04-14 | 2023-08-22 | 北京和瑞储能科技有限公司 | 基于支持向量回归的铁-铬液流电池性能预测方法与系统 |
| CN116148681A (zh) * | 2023-04-24 | 2023-05-23 | 北京和瑞储能科技有限公司 | 一种铁-铬液流电池性能预测方法 |
| KR20250085360A (ko) * | 2023-12-05 | 2025-06-12 | 주식회사 엘지에너지솔루션 | 배터리 관리 장치 및 배터리 관리 방법 |
| CN117725389B (zh) * | 2024-02-08 | 2024-06-21 | 宁德时代新能源科技股份有限公司 | 电池出站方法及电池出站系统 |
| KR20250147158A (ko) * | 2024-04-03 | 2025-10-13 | 주식회사 엘지에너지솔루션 | 배터리 진단 장치 및 배터리 진단 방법 |
| KR20250153006A (ko) * | 2024-04-17 | 2025-10-24 | 주식회사 엘지화학 | 배터리 수명 예측 장치 및 배터리 관리 방법 |
| TWI880775B (zh) * | 2024-06-11 | 2025-04-11 | 偉詮電子股份有限公司 | 電池電量計算方法及電池電量計算系統 |
| CN118625027A (zh) * | 2024-06-24 | 2024-09-10 | 深圳市美隆电子有限公司 | 一种电容器的电容性能测试方法及测试系统 |
| CN119001517B (zh) * | 2024-08-16 | 2025-04-04 | 苏州华电电气股份有限公司 | 一种基于环境模拟的大功率电源测试方法及系统 |
Family Cites Families (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100936892B1 (ko) * | 2007-09-13 | 2010-01-14 | 주식회사 엘지화학 | 배터리의 장기 특성 예측 시스템 및 방법 |
| US8301285B2 (en) * | 2011-10-31 | 2012-10-30 | Sakti3, Inc. | Computer aided solid state battery design method and manufacture of same using selected combinations of characteristics |
| KR101384847B1 (ko) * | 2013-06-26 | 2014-04-15 | 권동채 | 대용량 스마트 배터리의 가상검사시스템 |
| KR102424528B1 (ko) * | 2015-06-11 | 2022-07-25 | 삼성전자주식회사 | 배터리의 상태를 추정하는 장치 및 방법 |
| JP7018609B2 (ja) | 2015-06-26 | 2022-02-14 | 国立研究開発法人宇宙航空研究開発機構 | 電池の充電状態又は放電深度を推定する方法及びシステム、及び、電池の健全性を評価する方法及びシステム |
| US10191116B2 (en) * | 2015-10-15 | 2019-01-29 | Johnson Controls Technology Company | Battery test system for predicting battery test results |
| WO2018016531A1 (ja) | 2016-07-21 | 2018-01-25 | 日立化成株式会社 | 二次電池システム |
| EP3488512A1 (en) * | 2016-07-22 | 2019-05-29 | Eos Energy Storage, LLC | Battery management system |
| US10209314B2 (en) * | 2016-11-21 | 2019-02-19 | Battelle Energy Alliance, Llc | Systems and methods for estimation and prediction of battery health and performance |
| US11691518B2 (en) | 2017-07-21 | 2023-07-04 | Quantumscape Battery, Inc. | Predictive model for estimating battery states |
| US10992156B2 (en) | 2017-10-17 | 2021-04-27 | The Board Of Trustees Of The Leland Stanford Junior University | Autonomous screening and optimization of battery formation and cycling procedures |
| US11226374B2 (en) * | 2017-10-17 | 2022-01-18 | The Board Of Trustees Of The Leland Stanford Junior University | Data-driven model for lithium-ion battery capacity fade and lifetime prediction |
| US11171498B2 (en) * | 2017-11-20 | 2021-11-09 | The Trustees Of Columbia University In The City Of New York | Neural-network state-of-charge estimation |
| JP2019190905A (ja) | 2018-04-20 | 2019-10-31 | 株式会社Gsユアサ | 状態推定方法、及び状態推定装置 |
| CN108804800A (zh) * | 2018-06-04 | 2018-11-13 | 桂林电子科技大学 | 基于回声状态网络的锂离子电池soc在线预测方法 |
| CN110059844B (zh) * | 2019-02-01 | 2021-10-08 | 东华大学 | 基于集合经验模态分解和lstm的储能装置控制方法 |
| US11065978B2 (en) * | 2019-02-25 | 2021-07-20 | Toyota Research Institute, Inc. | Systems, methods, and storage media for adapting machine learning models for optimizing performance of a battery pack |
| CN110018882B (zh) * | 2019-03-29 | 2021-04-30 | 北京理工大学 | 一种基于宽度学习的虚拟机性能预测方法 |
| CN110224192B (zh) * | 2019-05-30 | 2021-02-12 | 安徽巡鹰新能源科技有限公司 | 一种梯次利用动力电池寿命预测方法 |
-
2020
- 2020-11-06 PL PL20799964.0T patent/PL4055399T3/pl unknown
- 2020-11-06 ES ES20799964T patent/ES3025683T3/es active Active
- 2020-11-06 CN CN202080077333.8A patent/CN114651183B/zh active Active
- 2020-11-06 KR KR1020227014915A patent/KR102902076B1/ko active Active
- 2020-11-06 JP JP2022526393A patent/JP7497432B2/ja active Active
- 2020-11-06 EP EP20799964.0A patent/EP4055399B1/en active Active
- 2020-11-06 US US17/774,169 patent/US20220373601A1/en active Pending
- 2020-11-06 HU HUE20799964A patent/HUE071081T2/hu unknown
- 2020-11-06 WO PCT/EP2020/081300 patent/WO2021089786A1/en not_active Ceased
-
2024
- 2024-01-26 JP JP2024010431A patent/JP2024059625A/ja not_active Withdrawn
Also Published As
| Publication number | Publication date |
|---|---|
| JP2023501995A (ja) | 2023-01-20 |
| JP2024059625A (ja) | 2024-05-01 |
| US20220373601A1 (en) | 2022-11-24 |
| EP4055399B1 (en) | 2025-02-26 |
| JP7497432B2 (ja) | 2024-06-10 |
| HUE071081T2 (hu) | 2025-07-28 |
| CN114651183A (zh) | 2022-06-21 |
| WO2021089786A1 (en) | 2021-05-14 |
| EP4055399C0 (en) | 2025-02-26 |
| ES3025683T3 (en) | 2025-06-09 |
| KR20220073829A (ko) | 2022-06-03 |
| KR102902076B1 (ko) | 2025-12-19 |
| CN114651183B (zh) | 2025-07-15 |
| EP4055399A1 (en) | 2022-09-14 |
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