Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System

Kandler Smith, Aron Saxon, Matthew Keyser, Blake Lundstrom, Ziwei Cao, Albert Roc

Research output: NRELPresentation

154 Scopus Citations


Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity and resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4% RMS error and resistance growth with 15% RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.
Original languageAmerican English
Number of pages24
StatePublished - 2017

Publication series

NamePresented at the 2017 American Control Conference, 23-26 May 2017, Seattle, Washington

NREL Publication Number

  • NREL/PR-5400-68759


  • aging
  • energy storage
  • life
  • lifetime
  • lithium-ion battery
  • modeling
  • photovoltaics
  • reliability


Dive into the research topics of 'Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System'. Together they form a unique fingerprint.

Cite this