  Cell inconsistency within a lithium-ion battery system poses a significant
challenge in maximizing the system operational time. This study presents an
optimization-driven active balancing method to minimize the effects of cell
inconsistency on the system operational time while simultaneously satisfying
the system output power demand and prolonging the system operational time in
energy storage applications. The proposed method utilizes a fractional order
model to forecast the terminal voltage dynamics of each cell within a battery
system, enhanced with a particle-swarm-optimisation-genetic algorithm for
precise parameter identification. It is implemented under two distinct
cell-level balancing topologies: independent cell balancing and differential
cell balancing. Subsequently, the current distribution for each topology is
determined by resolving two optimization control problems constrained by the
battery's operational specifications and power demands. The effectiveness of
the proposed method is validated by extensive experiments based on the two
balancing topologies. The results demonstrate that the proposed method
increases the operational time by 3.2%.
