Model-Based Prognostics For Batteries

Model-Based Prognostics For Batteries (TOP2-144)
Patent Only, No Software Available For License.
Battery-powered devices have become ubiquitous in the modern world, from tiny headsets, to cameras, cell phones and laptops, to hybrid and electric vehicles. Effective Battery Health Monitoring (BHM) technologies are needed to ensure that battery operation is optimal and, if not, that it stays within design limits and warnings are provided to operators or automated actions are taken to mitigate damage when these limits are exceeded. BHM technologies protect the assets batteries from degradation due to non-optimal usage. Prediction of the Remaining Useful Life (RUL) of a systems component is at the center of effectively managing a systems health. This invention provides as many as eight different prognostic modes for estimating the state of charge, state of life, end of discharge, and/or end of life of a battery. These estimates help in making predictions of the remaining useful life for individual discharge cycles as well as for cycle life.

The Technology
This invention relates to the prediction of the remaining useful life of an object in use. It develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The models used to estimate the remaining useful life of batteries are linked to the internal electro-chemical processes of the battery. The effects of temperature and load have been incorporated into the models. Model validation studies were conducted using data from a series of battery cycling experiments at various thermal and electrical loading conditions. Subsequently, the model has been used in a particle filtering framework to make probabilistic predictions of remaining useful life for individual discharge cycles as well as for cycle life.
Model-Based Prognostics For Batteries Battery environmental chamber
  • Provides enhanced health management routines for batteries
  • Provides mathematical rigorous reasoning framework for prognostics of battery RUL
  • Allows a variety of models to be accommodated
  • Produces RUL reports as output for the representation of the battery health
  • Provides accurate gauge for remaining electrical charge

  • Commercial concerns using batteries as the primary (or backup) power source for their product
  • Commercial R&D for prognostic health management /condition-based maintenance
  • Manufacturers of primarily batterypowered vehicles for land, air, and water
  • Companies developing software products for system health management
Technology Details

ARC-16320-1 ARC-16273-1
Model-based Prognostics with Concurrent Damage Progression Processes, 2013, IEEE Transactions on Systems, Man, and Cybernetics, Part A : Systems, Vol. 43 No. 4, 535-546.
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