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

Smith, Kandler; Shi, Ying; Santhanagopalan, Shriram

doi:10.1109/ACC.2015.7170820

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

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Abstract

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.

Authors:: Smith, Kandler; Shi, Ying; Santhanagopalan, Shriram

Publication Date:: Wed Jul 29 00:00:00 EDT 2015

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)

OSTI Identifier:: 1215242

Report Number(s):: NREL/CP-5400-64171

Resource Type:: Conference

Country of Publication:: United States

Language:: English

Subject:: 25 ENERGY STORAGE; battery; degradation; diagnostics; energy storage; lithium-ion; prognostics

Citation Formats


                    Smith, Kandler, Shi, Ying, and Santhanagopalan, Shriram. Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint.  United States: N. p., 2015. 
        Web.  doi:10.1109/ACC.2015.7170820.

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                    Smith, Kandler, Shi, Ying, & Santhanagopalan, Shriram. Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint.  United States.  https://doi.org/10.1109/ACC.2015.7170820

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                    Smith, Kandler, Shi, Ying, and Santhanagopalan, Shriram. 2015.  
        "Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint".  United States.  https://doi.org/10.1109/ACC.2015.7170820.  https://www.osti.gov/servlets/purl/1215242.

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@article{osti_1215242,

  title        = {Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint},

  author       = {Smith, Kandler and Shi, Ying and Santhanagopalan, Shriram},

  abstractNote = {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.},

  doi          = {10.1109/ACC.2015.7170820},

  url          = {https://www.osti.gov/biblio/1215242},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Wed Jul 29 00:00:00 EDT 2015},

  month        = {Wed Jul 29 00:00:00 EDT 2015}

}

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