TY - GEN
T1 - State of Charge Estimation of Zinc-Bromine Flow Batteries Using Physics-Informed Long Short-Term Memory Networks
AU - Peng, Kaiwen
AU - Wang, Liguo
AU - Sidorov, Denis
AU - Dreglea, Aliona
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - In order to improve the state of charge (SOC) estimation accuracy of zinc-bromine flow batteries during specific discharge phases and address the issue of large SOC estimation errors caused by rapid voltage drops and current fluctuations, this study proposes a physics-informed neural network-based SOC estimation algorithm. The proposed method integrates a first-order resistor-capacitor (RC) equivalent circuit model with a long short-term memory (LSTM) neural network, incorporating the parameters of the equivalent circuit model as part of the neural network parameters. Additionally, physical constraints are embedded into the loss function during network training, ensuring that the training process adheres to physical laws. The estimation results of the physics-informed LSTM (PILSTM) network achieve an average error of 0.90% and a mean squared error (MSE) of 1.52%, demonstrating that the proposed algorithm enables high-accuracy real-time SOC estimation for zinc-bromine flow batteries.
AB - In order to improve the state of charge (SOC) estimation accuracy of zinc-bromine flow batteries during specific discharge phases and address the issue of large SOC estimation errors caused by rapid voltage drops and current fluctuations, this study proposes a physics-informed neural network-based SOC estimation algorithm. The proposed method integrates a first-order resistor-capacitor (RC) equivalent circuit model with a long short-term memory (LSTM) neural network, incorporating the parameters of the equivalent circuit model as part of the neural network parameters. Additionally, physical constraints are embedded into the loss function during network training, ensuring that the training process adheres to physical laws. The estimation results of the physics-informed LSTM (PILSTM) network achieve an average error of 0.90% and a mean squared error (MSE) of 1.52%, demonstrating that the proposed algorithm enables high-accuracy real-time SOC estimation for zinc-bromine flow batteries.
KW - long short-term memory
KW - physics-informed nerual networks
KW - state of charge
KW - zinc-bromine flow batteries
UR - https://www.scopus.com/pages/publications/105010816683
U2 - 10.1109/ICMTIM65484.2025.11040212
DO - 10.1109/ICMTIM65484.2025.11040212
M3 - 会议稿件
AN - SCOPUS:105010816683
T3 - 2025 6th International Conference on Mechatronics Technology and Intelligent Manufacturing, ICMTIM 2025
SP - 47
EP - 51
BT - 2025 6th International Conference on Mechatronics Technology and Intelligent Manufacturing, ICMTIM 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Mechatronics Technology and Intelligent Manufacturing, ICMTIM 2025
Y2 - 11 April 2025 through 13 April 2025
ER -