TY - JOUR
T1 - Data-Driven Power Sharing Control within MultiSource Paralleled DAB Converters
AU - Liu, Yuyang
AU - Yang, Tao
AU - Hao, Xinyang
AU - Qi, Yang
AU - Li, Weilin
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2025
Y1 - 2025
N2 - To achieve redundancy requirements for transportation electrification, a very commonly used method is to have multiple energy storage sources connected in parallel and feed a common DC bus through DC/DC power converters. The dual-active bridge (DAB) converter, in that respect, is an appealing topology to interface these energy storage elements with the main DC grid due to its simple structure and high efficiency. However, conventional optimum control has limitations in the power sharing control scheme. To address these challenges, this paper proposes a data-driven method using the artificial neural network (ANN) to generate the optimal control solutions and achieve desired current sharing among parallel DABs. The design process begins with data generation from extensive DAB simulations. Subsequently, the derived data is processed and clustered, such to map DAB phase shifts with its power output. Then the ANN is used to train a surrogate model to map the power demand and phase shifts. With the aid of ANN model, the small-signal model of the DAB converter is also simplified. Finally, the proposed method is implemented in a three-DAB system. The current sharing performance, efficiency and the small-signal model have been proved by experiments.
AB - To achieve redundancy requirements for transportation electrification, a very commonly used method is to have multiple energy storage sources connected in parallel and feed a common DC bus through DC/DC power converters. The dual-active bridge (DAB) converter, in that respect, is an appealing topology to interface these energy storage elements with the main DC grid due to its simple structure and high efficiency. However, conventional optimum control has limitations in the power sharing control scheme. To address these challenges, this paper proposes a data-driven method using the artificial neural network (ANN) to generate the optimal control solutions and achieve desired current sharing among parallel DABs. The design process begins with data generation from extensive DAB simulations. Subsequently, the derived data is processed and clustered, such to map DAB phase shifts with its power output. Then the ANN is used to train a surrogate model to map the power demand and phase shifts. With the aid of ANN model, the small-signal model of the DAB converter is also simplified. Finally, the proposed method is implemented in a three-DAB system. The current sharing performance, efficiency and the small-signal model have been proved by experiments.
KW - Artificial neural networks (ANNs)
KW - dual active bridge converter
KW - dual-phase shift
KW - efficiency
KW - power balance control
UR - http://www.scopus.com/inward/record.url?scp=105004074393&partnerID=8YFLogxK
U2 - 10.1109/TTE.2025.3565006
DO - 10.1109/TTE.2025.3565006
M3 - 文章
AN - SCOPUS:105004074393
SN - 2332-7782
JO - IEEE Transactions on Transportation Electrification
JF - IEEE Transactions on Transportation Electrification
M1 - 0b00006493e0d3bb
ER -