TY - GEN
T1 - Multi-Layer Control for Hybrid Balancing Systems
AU - De Castro, Ricardo
AU - Pereira, Helder
AU - Araujo, Rui Esteves
AU - Barreras, Jorge Varela
AU - Pangborn, Herschel C.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Hybrid balancing is a recently-proposed class of battery balancing systems that simultaneously provide capacity and thermal equalization, while enabling hybridization with supercapacitors. This integration of functions poses a challenging control problem, requiring the fulfillment of multiple objectives (e.g., reduction of charge and temperature imbalances, energy losses and battery stress) and the coordination of a large number of power converters. To tackle this challenge, we propose a multi-layer model predictive control (MPC) framework, which splits the control tasks into two layers. The first layer uses long prediction horizons and a simplified model of the energy storage system to compute the state-of-charge reference for the supercapacitors. The second layer uses module-level models of the battery pack to track this reference, while minimizing charge and temperature imbalances with a small prediction horizon. Simulation results demonstrate that the multi-layer MPC provides similar performance as single-layer MPC, but at a fraction of the computational effort.
AB - Hybrid balancing is a recently-proposed class of battery balancing systems that simultaneously provide capacity and thermal equalization, while enabling hybridization with supercapacitors. This integration of functions poses a challenging control problem, requiring the fulfillment of multiple objectives (e.g., reduction of charge and temperature imbalances, energy losses and battery stress) and the coordination of a large number of power converters. To tackle this challenge, we propose a multi-layer model predictive control (MPC) framework, which splits the control tasks into two layers. The first layer uses long prediction horizons and a simplified model of the energy storage system to compute the state-of-charge reference for the supercapacitors. The second layer uses module-level models of the battery pack to track this reference, while minimizing charge and temperature imbalances with a small prediction horizon. Simulation results demonstrate that the multi-layer MPC provides similar performance as single-layer MPC, but at a fraction of the computational effort.
UR - http://www.scopus.com/inward/record.url?scp=85111160238&partnerID=8YFLogxK
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U2 - 10.1109/CCTA48906.2021.9658882
DO - 10.1109/CCTA48906.2021.9658882
M3 - Conference contribution
AN - SCOPUS:85111160238
T3 - CCTA 2021 - 5th IEEE Conference on Control Technology and Applications
SP - 839
EP - 845
BT - CCTA 2021 - 5th IEEE Conference on Control Technology and Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE Conference on Control Technology and Applications, CCTA 2021
Y2 - 8 August 2021 through 11 August 2021
ER -