US 12,487,289 B2
Systems and methods for determining the chargeable or dischargeable energy of a battery energy storage system
Wonbin Choi, Westborough, MA (US); and Kiran Kumar, Westborough, MA (US)
Assigned to LG ENERGY SOLUTION, LTD., Seoul (KR)
Filed by LG ENERGY SOLUTION, LTD., Seoul (KR)
Filed on Nov. 21, 2024, as Appl. No. 18/955,372.
Claims priority of provisional application 63/601,641, filed on Nov. 21, 2023.
Prior Publication US 2025/0164563 A1, May 22, 2025
Int. Cl. G01R 31/367 (2019.01); G01R 31/36 (2020.01); G01R 31/3842 (2019.01); G01R 31/396 (2019.01)
CPC G01R 31/367 (2019.01) [G01R 31/3648 (2013.01); G01R 31/3842 (2019.01); G01R 31/396 (2019.01)] 18 Claims
OG exemplary drawing
 
1. A system for determining a total chargeable/dischargeable energy of a subsystem of a battery energy storage system (BESS), the subsystem comprising battery cells, comprising:
one or more controllers comprising one or more processing modules and one or more non-transitory memory storage modules storing computing instructions which when executed by the one or more processing modules is configured to:
execute an iterative process over a dynamic time period, wherein the dynamic time period is divided into iterations, by using a neural network model comprising an energy prediction sub-model and a state prediction sub-model, wherein for each iteration of the iterations, the controller is configured to:
(1) input into the energy prediction sub-model: a voltage of the subsystem for a current iteration of the iterations, a charge rate of the subsystem for the current iteration, and a maximum temperature of the subsystem for the current iteration;
wherein the energy prediction sub-model is configured to output a chargeable/dischargeable energy of the subsystem for the current iteration; and
(2) input into the state prediction sub-model: the voltage of the subsystem for the current iteration, the charge rate of the subsystem for the current iteration, the maximum temperature of the subsystem for the current iteration, and a charge rate difference for the current iteration;
wherein the charge rate difference for the current iteration is the charge rate of the subsystem for the current iteration minus the charge rate of the subsystem for a previous iteration of the iterations;
wherein the state prediction sub-model is configured to output a voltage of the subsystem for a next iteration of the iterations, the charge rate of the subsystem for the next iteration, the maximum temperature of the subsystem for the next iteration, and a charge rate difference for the next iteration;
(3) repeat steps (1) and (2) until a last iteration of the iterative process is executed; and
(4) determine the total chargeable/dischargeable energy of the subsystem of the BESS over the dynamic time period by summing the chargeable/dischargeable energy outputted by the energy prediction sub-model for each iteration;
(5) manage a timing and an amount of energy for charging and discharging the BESS based at least partially on the determined total chargeable/dischargeable energy of the subsystem of the BESS.