  Battery Asset Management problem determines the minimum cost replacement
schedules for each individual asset in a group of battery assets that operate
in parallel. Battery cycle life varies under different operating conditions
including temperature, depth of discharge, charge rate, etc., and a battery
deteriorates due to usage, which cannot be handled by current asset management
models. This paper presents battery cycle life prognosis and its integration
with parallel asset management to reduce lifecycle cost of the Battery Energy
Storage System (BESS). A nonlinear capacity fade model is incorporated in the
parallel asset management model to update battery capacity. Parametric studies
have been conducted to explore the influence of different model inputs (e.g.
usage rate, unit battery capacity, operating condition and periodical demand)
for a five-year time horizon. Experiment results verify the reasonableness of
this new framework and suggest that the increase in battery lifetime leads to
decrease in lifecycle cost.
