TY - JOUR
T1 - qTSL
T2 - A Multilayer Control Framework for Managing Capacity, Temperature, Stress, and Losses in 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:
© 2022 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - This work deals with the design and validation of a control strategy for hybrid balancing systems (HBSs), an emerging concept that joins battery equalization and hybridization with supercapacitors (SCs) in the same system. To control this system, we propose a two-layer model predictive control (MPC) framework. The first layer determines the optimal state-of-charge (SoC) reference for the SCs considering long load forecasts and simple pack-level battery models. The second MPC layer tracks this reference and performs charge and temperature equalization, employing more complex module-level battery models and short load forecasts. This division of control tasks into two layers, running at different time scales and model complexities, enables us to reduce computational effort with a small loss of control performance. Experimental validation in a small-scale laboratory prototype demonstrates the effectiveness of the proposed approach in reducing charge, temperature, and stress in the battery pack.
AB - This work deals with the design and validation of a control strategy for hybrid balancing systems (HBSs), an emerging concept that joins battery equalization and hybridization with supercapacitors (SCs) in the same system. To control this system, we propose a two-layer model predictive control (MPC) framework. The first layer determines the optimal state-of-charge (SoC) reference for the SCs considering long load forecasts and simple pack-level battery models. The second MPC layer tracks this reference and performs charge and temperature equalization, employing more complex module-level battery models and short load forecasts. This division of control tasks into two layers, running at different time scales and model complexities, enables us to reduce computational effort with a small loss of control performance. Experimental validation in a small-scale laboratory prototype demonstrates the effectiveness of the proposed approach in reducing charge, temperature, and stress in the battery pack.
UR - http://www.scopus.com/inward/record.url?scp=85113301083&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85113301083&partnerID=8YFLogxK
U2 - 10.1109/TCST.2021.3103483
DO - 10.1109/TCST.2021.3103483
M3 - Article
AN - SCOPUS:85113301083
SN - 1063-6536
VL - 30
SP - 1228
EP - 1243
JO - IEEE Transactions on Control Systems Technology
JF - IEEE Transactions on Control Systems Technology
IS - 3
ER -