US 12,444,962 B2
Methods and systems for detecting faulty behavior in a battery
Samarth Agarwal, Bengaluru (IN); Seongho Han, Suwon-si (KR); Krishnan Hariharan, Bengaluru (IN); and Achyutha Krishna Koneti, Bengaluru (IN)
Assigned to SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed by SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed on Mar. 22, 2022, as Appl. No. 17/701,263.
Application 17/701,263 is a continuation of application No. PCT/KR2021/013312, filed on Sep. 29, 2021.
Claims priority of application No. 202041043385 (IN), filed on Oct. 6, 2020; and application No. 202041043385 (IN), filed on Sep. 14, 2021.
Prior Publication US 2022/0216700 A1, Jul. 7, 2022
Int. Cl. H02J 7/00 (2006.01); B60L 3/00 (2019.01); G01R 31/392 (2019.01)
CPC H02J 7/005 (2020.01) [B60L 3/0046 (2013.01); G01R 31/392 (2019.01); H02J 7/0048 (2020.01); B60L 2240/547 (2013.01); B60L 2240/549 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A method for detecting at least one anomaly in a battery, the method comprising:
obtaining, by a processor, charging-discharging data of the battery that has undergone a preset number of charging-discharging cycles;
obtaining, by the processor, a probability of the battery being healthy and at least one probability of the battery having an anomaly of at least one class, based on a correlation between charging-discharging data of a plurality of reference batteries and the charging-discharging data of the battery;
obtaining, by the processor, a reliability index indicating a level of reliability of usage of the battery based on the probability of the battery being healthy, the at least one probability of the battery having anomaly of the at least one class;
obtaining, by the processor, at least one anomaly class index indicating at least one level of anomaly of the at least one class based on the probability of the battery being healthy, the at least one probability of the battery having the anomaly of the at least one class; and
alerting a user of an electronic device including the battery based on the reliability index and the at least one anomaly class index,
wherein the at least one anomaly class index comprises at least one of bending index, swelling index or dent index.