Battery Asset Management with Cycle Life Prognosis

Xinyang Liu, Zhuoyuan Zheng, Esra Büyüktahtakın, Zhi Zhou, Pingfeng Wang

Research output: Contribution to journalArticlepeer-review


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 (DOD), charge rate, etc., and a battery deteriorates due to usage, which cannot be handled by current asset management models. This paper presents a new battery asset management methodology where battery cycle life prognosis is integrated with parallel asset management to reduce lifecycle cost of the Battery Energy Storage Systems (BESS). For the battery failure time prognosis, a nonlinear physics-based battery capacity fade model is developed and incorporated in parallel asset management model to update battery capacity over time. Experiment results have shown that the developed battery asset management methodology can be conveniently used to facilitate BESS asset management decision making thereby decreasing asset lifecycle costs.

Original languageEnglish (US)
Article number107948
JournalReliability Engineering and System Safety
StatePublished - Dec 2021


  • Battery energy storage system
  • Integer programming
  • Lifetime prediction
  • Parallel asset management
  • Prognosis

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering

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