Battery Asset Management with Cycle Life Prognosis

TL;DR

This paper introduces a novel framework integrating battery cycle life prognosis with asset management to optimize replacement schedules and reduce lifecycle costs of Battery Energy Storage Systems.

Contribution

It presents a new nonlinear capacity fade model integrated into asset management, accounting for operating conditions and usage to improve lifecycle cost predictions.

Findings

Increased battery lifetime reduces lifecycle costs.

The model accurately predicts capacity fade under various conditions.

Parametric studies highlight key factors affecting battery longevity.

Abstract

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.