On-Board Prediction of Remaining Useful Life of Lithium-Ion Battery

This project was intended to create an intelligent prognostics platform for lithium-ion (Li-ion) batteries, which would equip existing battery management systems with the capability to perform predictive maintenance/control for failure prevention. The platform developed in this project consisted of two modules: • Deep feature learning, which automatically learns the features of (capacity) fade from large volumes of voltage and current measurement data during partial charge cycles and estimates the real-time state of health (SOH) of a battery cell in operation • Ensemble prognostics, which leverage the current and past SOH estimates in Module 1 to achieve robust prediction of the cell’s remaining useful life Robust prediction of remaining useful life was achieved by ensemble learning-based prognostics, which synthesized the generalization strengths of multiple prognostic algorithms to ensure high prediction accuracy for an expanded range of battery applications and their operating conditions. The two modules aimed to learn features of fade from partial charge data, assess real-time health of individual battery cells, and predict when and how the cells are likely to fail. A case study involving implantable-grade Li-ion cells was conducted to demonstrate a deep learning approach to online capacity estimation, developed for Module 1.

Language

  • English

Project

  • Status: Completed
  • Funding: $100258
  • Contract Numbers:

    DTRT13-G-UTC37

  • Sponsor Organizations:

    Midwest Transportation Center

    Iowa State University
    2711 S Loop Drive, Suite 4700
    Ames, IA  United States  50010-8664

    Iowa State University, Ames

    Institute for Transportation
    2711 South Loop Drive, Suite 4700
    Ames, Iowa  United States  50010-8664

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Midwest Transportation Center

    Iowa State University
    2711 S Loop Drive, Suite 4700
    Ames, IA  United States  50010-8664
  • Performing Organizations:

    Iowa State University, Ames

    Institute for Transportation
    2711 South Loop Drive, Suite 4700
    Ames, Iowa  United States  50010-8664
  • Principal Investigators:

    Hu, Chao

  • Start Date: 20160301
  • Expected Completion Date: 20190131
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01595722
  • Record Type: Research project
  • Source Agency: Midwest Transportation Center
  • Contract Numbers: DTRT13-G-UTC37
  • Files: UTC, RiP
  • Created Date: Apr 5 2016 9:52AM