Deep Learning Method for Lithium-Ion Battery Health Monitoring in Electric Propulsion

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SYSTEM AND METHOD FOR IN-OPERANDO HEALTH MONITORING FOR LITHIUM-ION BATTERIES IN ELECTRIC PROPULSION USING DEEP LEARNING

Application US20260098906A1 Kind: A1 Apr 09, 2026

Assignee

Purdue Research Foundation

Inventors

Vikas Tomar, Meghana Sudarshan, Alexey Yourievich Serov, Jaya Vikeswara Rao Vajja

Abstract

A method of predicting battery end of life based on a small dataset of sensor data include training a deep learning network using a plurality of a priori generated training datasets, receiving sensor data from a plurality of sensors in real-time coupled to one or more cells in a battery pack as the one or more cells are used in a present discharge cycle to thereby generate a plurality of new unseen sensor datapoints, and applying the new unseen sensor datapoints to the trained deep learning network to thereby generate operational recommendations to achieve one or both of i) extend end of life of the battery pack, and ii) extend operational time of the battery pack during the present discharge cycle or a future discharge cycle.

CPC Classifications

G01R 31/367 G01R 31/374 G01R 31/3842 G01R 31/392 G01R 31/396 G06N 3/09 H01M 10/425 H01M 10/486 H01M 2010/4271

Filing Date

2025-10-02

Application No.

19348739

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