  Large-scale battery energy storage systems (BESS) are helping transition the
world towards sustainability with their broad use, among others, in electrified
transportation, power grid, and renewables. However, optimal power management
for them is often computationally formidable. To overcome this challenge, we
develop a scalable approach in the paper. The proposed approach partitions the
constituting cells of a large-scale BESS into clusters based on their
state-of-charge (SoC), temperature, and internal resistance. Each cluster is
characterized by a representative model that approximately captures its
collective SoC and temperature dynamics, as well as its overall power losses in
charging/discharging. Based on the clusters, we then formulate a problem of
receding-horizon optimal power control to minimize the power losses while
promoting SoC and temperature balancing. The cluster-based power optimization
will decide the power quota for each cluster, and then every cluster will split
the quota among the constituent cells. Since the number of clusters is much
fewer than the number of cells, the proposed approach significantly reduces the
computational costs, allowing optimal power management to scale up to
large-scale BESS. Extensive simulations are performed to evaluate the proposed
approach. The obtained results highlight a significant computational overhead
reduction by more than 60% for a small-scale and 98% for a large-scale BESS
compared to the conventional cell-level optimization. Experimental validation
based on a 20-cell prototype further demonstrates its effectiveness and
utility.
