US 12,228,613 B1
Battery system state of health prediction modeling
Siddharth Satpathy, San Jose, CA (US); Chandan Gope, Cupertino, CA (US); and Yoosok Saw, Mountain View, CA (US)
Assigned to Element Energy, Inc., Menlo Park, CA (US)
Filed by Element Energy, Inc., Menlo Park, CA (US)
Filed on Apr. 15, 2024, as Appl. No. 18/635,881.
Int. Cl. G01R 31/367 (2019.01); G01R 31/36 (2020.01); G01R 31/378 (2019.01); G01R 31/392 (2019.01)
CPC G01R 31/367 (2019.01) [G01R 31/3648 (2013.01); G01R 31/392 (2019.01); G01R 31/378 (2019.01)] 18 Claims
OG exemplary drawing
 
1. A method of controlling and managing a target battery system, the method comprising:
receiving via a communication interface a request to train the machine learning model for the target battery system, the request identifying configuration information for the target battery system;
selecting a reference battery system from a plurality of reference battery systems based on the configuration information, the reference battery system sharing one or more characteristics with the target battery system;
identifying via a processor reference initialization data generated by the reference battery system;
determining synthetic training data for the target battery system via a processor by: (1) determining a first set of simulated input parameters, and (2) determining a second set of simulated output parameters by supplying the first set of simulated input parameters to a physics model;
determining via a processor whether a first subset of the reference initialization data matches a second subset of the synthetic training data;
training a machine learning model based on the synthetic training data via a processor upon determining that the first subset of the reference initialization data matches the second subset of the synthetic training data;
applying the trained machine learning model to determine a predicted future state of the target battery system based at least in part on one or more observed input parameters generated by the target battery system;
determining a designated control parameter based on the predicted future state of the target battery system, the designated control parameter selected from the group consisting of: a charge voltage profile, a discharge voltage profile, disabling one or more battery cells, and bypassing one or more battery cells; and
causing the target battery system to implement the designated control parameter by transmitting an instruction to the target battery system via the communication interface.