US 11,705,590 B1
Systems and methods for predicting remaining useful life in batteries and assets
Gokhan Budan, Kidlington (GB); Anil Ozturk, Istanbul (TR); Alex Darlington, Leamington Spa (GB); and Can Kurtulus, Istanbul (TR)
Assigned to Eatron Technologies Ltd., Warwick (GB)
Filed by EATRON TECHNOLOGIES LIMITED, Warwick (GB)
Filed on Aug. 15, 2022, as Appl. No. 17/887,865.
Application 17/887,865 is a continuation of application No. 17/706,172, filed on Mar. 28, 2022, granted, now 11,527,786.
This patent is subject to a terminal disclaimer.
Int. Cl. H01M 10/48 (2006.01); B60L 58/16 (2019.01); G01R 31/367 (2019.01); G01R 31/396 (2019.01); G01R 31/392 (2019.01); G07C 5/04 (2006.01); G01R 31/3842 (2019.01)
CPC H01M 10/482 (2013.01) [B60L 58/16 (2019.02); G01R 31/367 (2019.01); G01R 31/3842 (2019.01); G01R 31/392 (2019.01); G01R 31/396 (2019.01); G07C 5/04 (2013.01); H01M 10/486 (2013.01); B60L 2240/545 (2013.01); B60L 2240/547 (2013.01); B60L 2240/549 (2013.01); H01M 2220/20 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, from a cloud-based computing system, one or more machine learning model parameters that are configured to enable predicting a remaining useful life of each cell of a battery pack of a vehicle, wherein the one or more machine learning model parameters are selected based on iteratively evaluating performance of a first trained machine learning model executed by the cloud-based computing system;
loading, into memory of a processing device at the vehicle, the one or more machine learning model parameters;
receiving data comprising one or more measurements and one or more user battery usage profiles; and
based on the data, executing a second trained machine learning model with the one or more parameters to input the data and to output the remaining useful life of each cell of the battery pack, wherein the second trained machine learning model is executed by the processing device at the vehicle.