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Kouhestani et al., 2023 - Google Patents

Data-driven prognosis of failure detection and prediction of lithium-ion batteries

Kouhestani et al., 2023

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Document ID
5641973841899931705
Author
Kouhestani H
Liu L
Wang R
Chandra A
Publication year
Publication venue
Journal of Energy Storage

External Links

Snippet

Battery prognostics and health management predictive models are essential components of safety and reliability protocols in battery management system frameworks. Overall, developing a robust and efficient fault diagnostic battery model that aligns with the current …
Continue reading at www.sciencedirect.com (HTML) (other versions)

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
    • G01R31/36Apparatus for testing electrical condition of accumulators or electric batteries, e.g. capacity or charge condition
    • G01R31/3644Various constructional arrangements
    • G01R31/3648Various constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • G01R31/3651Software aspects, e.g. battery modeling, using look-up tables, neural networks
    • 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
    • G01R31/36Apparatus for testing electrical condition of accumulators or electric batteries, e.g. capacity or charge condition
    • G01R31/3644Various constructional arrangements
    • G01R31/3679Various constructional arrangements for determining battery ageing or deterioration, e.g. state-of-health (SoH), state-of-life (SoL)

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