TY - GEN
T1 - Reconfiguration-assisted charging in large-scale Lithium-ion battery systems
AU - He, Liang
AU - Kong, Linghe
AU - Lin, Siyu
AU - Ying, Shaodong
AU - Gu, Yu
AU - He, Tian
AU - Liu, Cong
PY - 2014
Y1 - 2014
N2 - Large-scale Lithium-ion batteries are widely adopted in many systems such as electric vehicles and energy backup in power grids. Due to factors such as manufacturing difference and heterogeneous discharging conditions, cells in the battery system may have different statuses such as diverse voltage levels. This cell diversity is commonly known as the cell unbalance issue, which becomes more critical as the system scale increases. The cell unbalance issue not only significantly degrades the system performance in many aspects, but may also cause system safety issues such as the burning of battery cells and thus increase system vulnerability. In this paper, based on the advancement in reconfig-urable battery systems, we demonstrate how to utilize system reconfiguration flexibility for achieving an efficient charging process for the battery system. With the proposed reconfiguration-assisted charging, the cells in the system are categorized according to their voltages, and the charging process is evolutionarily carried out in a category-by-category manner. For the charging of cells in a given category, a graph-based algorithm is presented to obtain the desired system configuration. We extensively evaluate the reconfiguration-assisted charging through small-scale implementation and large-scale trace-driven simulations. The results demonstrate that our proposed techniques can achieve a 25% increase on average on charged capacities of individual cells while yielding a dramatically reduced variance.
AB - Large-scale Lithium-ion batteries are widely adopted in many systems such as electric vehicles and energy backup in power grids. Due to factors such as manufacturing difference and heterogeneous discharging conditions, cells in the battery system may have different statuses such as diverse voltage levels. This cell diversity is commonly known as the cell unbalance issue, which becomes more critical as the system scale increases. The cell unbalance issue not only significantly degrades the system performance in many aspects, but may also cause system safety issues such as the burning of battery cells and thus increase system vulnerability. In this paper, based on the advancement in reconfig-urable battery systems, we demonstrate how to utilize system reconfiguration flexibility for achieving an efficient charging process for the battery system. With the proposed reconfiguration-assisted charging, the cells in the system are categorized according to their voltages, and the charging process is evolutionarily carried out in a category-by-category manner. For the charging of cells in a given category, a graph-based algorithm is presented to obtain the desired system configuration. We extensively evaluate the reconfiguration-assisted charging through small-scale implementation and large-scale trace-driven simulations. The results demonstrate that our proposed techniques can achieve a 25% increase on average on charged capacities of individual cells while yielding a dramatically reduced variance.
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U2 - 10.1109/ICCPS.2014.6843711
DO - 10.1109/ICCPS.2014.6843711
M3 - Conference contribution
AN - SCOPUS:84904512296
SN - 9781479949311
T3 - 2014 ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2014
SP - 60
EP - 71
BT - 2014 ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2014
PB - IEEE Computer Society
T2 - 5th IEEE/ACM International Conference on Cyber-Physical Systems, ICCPS 2014
Y2 - 14 April 2014 through 17 April 2014
ER -