Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective

Kandler Smith, Ying Shi, Shriram Santhanagopalan

Research output: Contribution to conferencePaperpeer-review

24 Scopus Citations


Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under different levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.

Original languageAmerican English
Number of pages3
StatePublished - 28 Jul 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: 1 Jul 20153 Jul 2015


Conference2015 American Control Conference, ACC 2015
Country/TerritoryUnited States

Bibliographical note

See NREL/CP-5400-64171 for preprint

NREL Publication Number

  • NREL/CP-5400-65195


  • battery
  • degradation
  • diagnostics
  • energy storage
  • lithium-ion
  • prognostics


Dive into the research topics of 'Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective'. Together they form a unique fingerprint.

Cite this