TY - JOUR
T1 - A robust machine learning-based SOC estimation approach for vanadium redox flow battery
AU - Zheng, Chengyan
AU - Feng, Wendong
AU - Wei, Zhongbao
AU - Li, Yifeng
AU - Iu, Herbert Ho Ching
AU - Fernando, Tyrone
AU - Zhang, Xinan
N1 - Publisher Copyright:
© 2025
PY - 2025/7/30
Y1 - 2025/7/30
N2 - The vanadium redox flow battery (VRB) is recognized as an effective large-scale energy storage solution for mitigating the renewable intermittency and ensuring grid reliability. Accurate estimation of the state of charge (SOC) is crucial for the optimal operation of VRB. This paper presents a novel machine learning-based estimation algorithm to overcome the long-lasting problem of model dependency in the existing SOC estimation approaches for VRB. Compared to the conventional model based methods, such as Kalman filter and sliding mode observer, the proposed algorithm does not need any knowledge of the VRB model. In addition, the proposed algorithm employs recurrent equilibrium network (REN), which has “built in” behavioral guarantees of stability and robustness compared to the traditional machine learning algorithms. Furthermore, the proposed algorithm employs the nonlinear direct parameterization technique to substantially simplify the neural network training. Its efficacy is verified by experimental results.
AB - The vanadium redox flow battery (VRB) is recognized as an effective large-scale energy storage solution for mitigating the renewable intermittency and ensuring grid reliability. Accurate estimation of the state of charge (SOC) is crucial for the optimal operation of VRB. This paper presents a novel machine learning-based estimation algorithm to overcome the long-lasting problem of model dependency in the existing SOC estimation approaches for VRB. Compared to the conventional model based methods, such as Kalman filter and sliding mode observer, the proposed algorithm does not need any knowledge of the VRB model. In addition, the proposed algorithm employs recurrent equilibrium network (REN), which has “built in” behavioral guarantees of stability and robustness compared to the traditional machine learning algorithms. Furthermore, the proposed algorithm employs the nonlinear direct parameterization technique to substantially simplify the neural network training. Its efficacy is verified by experimental results.
KW - Direct parameterization
KW - Recurrent equilibrium network
KW - SOC estimation
KW - Vanadium redox flow battery
UR - http://www.scopus.com/inward/record.url?scp=105003954600&partnerID=8YFLogxK
U2 - 10.1016/j.jpowsour.2025.237087
DO - 10.1016/j.jpowsour.2025.237087
M3 - Article
AN - SCOPUS:105003954600
SN - 0378-7753
VL - 645
JO - Journal of Power Sources
JF - Journal of Power Sources
M1 - 237087
ER -