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, discharge rate, etc. and 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 battery energy storage systems. Battery cycle life is predicted as a function of temperature, depth of discharge and discharge rate based on experimental data. Aging index of the battery is then determined and incorporated in parallel asset management model. Experiment results verify the effectiveness of this new framework and suggest that the increase in battery lifetime leads to decrease in lifecycle cost.