GB202020829D0 - Method for predicting condition parameter degradation of a cell - Google Patents

Method for predicting condition parameter degradation of a cell

Info

Publication number
GB202020829D0
GB202020829D0 GBGB2020829.4A GB202020829A GB202020829D0 GB 202020829 D0 GB202020829 D0 GB 202020829D0 GB 202020829 A GB202020829 A GB 202020829A GB 202020829 D0 GB202020829 D0 GB 202020829D0
Authority
GB
United Kingdom
Prior art keywords
cell
condition parameter
parameter degradation
predicting condition
predicting
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
GBGB2020829.4A
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Edinburgh
Original Assignee
University of Edinburgh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Edinburgh filed Critical University of Edinburgh
Priority to GBGB2020829.4A priority Critical patent/GB202020829D0/en
Publication of GB202020829D0 publication Critical patent/GB202020829D0/en
Priority to PCT/GB2021/053437 priority patent/WO2022144542A1/en
Ceased legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392Determining battery ageing or deterioration, e.g. state of health
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367Software therefor, e.g. for battery testing using modelling or look-up tables
GBGB2020829.4A 2020-12-31 2020-12-31 Method for predicting condition parameter degradation of a cell Ceased GB202020829D0 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
GBGB2020829.4A GB202020829D0 (en) 2020-12-31 2020-12-31 Method for predicting condition parameter degradation of a cell
PCT/GB2021/053437 WO2022144542A1 (en) 2020-12-31 2021-12-24 Method for predicting condition parameter degradation of a cell

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
GBGB2020829.4A GB202020829D0 (en) 2020-12-31 2020-12-31 Method for predicting condition parameter degradation of a cell

Publications (1)

Publication Number Publication Date
GB202020829D0 true GB202020829D0 (en) 2021-02-17

Family

ID=74566505

Family Applications (1)

Application Number Title Priority Date Filing Date
GBGB2020829.4A Ceased GB202020829D0 (en) 2020-12-31 2020-12-31 Method for predicting condition parameter degradation of a cell

Country Status (2)

Country Link
GB (1) GB202020829D0 (en)
WO (1) WO2022144542A1 (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113839107B (en) * 2021-09-22 2023-03-21 北京航空航天大学 Early warning method for soft package lithium ion battery diving degradation mode
CN115508711B (en) * 2022-10-10 2023-06-30 南京工程学院 Electric vehicle power lithium battery residual life prediction method based on deep network model

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
ATTIA, P. M. ET AL.: "Closed-loop optimization of fast-charging protocols for batteries with machine learning", NATURE, vol. 578, 2020, pages 397 - 402, XP037035978, DOI: 10.1038/s41586-020-1994-5
FERMIN, P ET AL.: "Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells", ENERGY AND AL, 2020, pages 100006, Retrieved from the Internet <URL:https://doi.Org/10.1016/j.egyai.2020.100006>
SEVERSON, K ET AL.: "Data-driven prediction of battery cycle life before capacity degradation", NATURE ENERGY, vol. 4, 2019, pages 1 - 9

Also Published As

Publication number Publication date
WO2022144542A1 (en) 2022-07-07

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Legal Events

Date Code Title Description
AT Applications terminated before publication under section 16(1)