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

Kandler Smith, Ying Shi, Shriram Santhanagopalan

Research output: Contribution to conferencePaper


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 pages5
StatePublished - 2015
Event2015 American Control Conference - Chicago, Illinois
Duration: 1 Jul 20153 Jul 2015


Conference2015 American Control Conference
CityChicago, Illinois

NREL Publication Number

  • NREL/CP-5400-64171


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


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