Friday, May 29, 2015

On Uncertainty Quantification of Lithium-ion Batteries

Forget devising the exact location of atoms, people, computing unimaginable quantum mechanical probability distributions, what if compressive sensing enabled us to figure out how to extend the lifetime of our smartphone batteries ? This is what this uncertainty quantification study using compressive sensing tries to do.

Figure from this tweet.

In this work, a stochastic, physics-based model for Lithium-ion batteries (LIBs) is presented in order to study the effects of model uncertainties on the cell capacity, voltage, and concentrations. To this end, the proposed uncertainty quantification (UQ) approach, based on sparse polynomial chaos expansions, relies on a small number of battery simulations. Within this UQ framework, the identification of most important uncertainty sources is achieved by performing a global sensitivity analysis via computing the so-called Sobol' indices. Such information aids in designing more efficient and targeted quality control procedures, which consequently may result in reducing the LIB production cost. An LiC6/LiCoO2 cell with 19 uncertain parameters discharged at 0.25C, 1C and 4C rates is considered to study the performance and accuracy of the proposed UQ approach. The results suggest that, for the considered cell, the battery discharge rate is a key factor affecting not only the performance variability of the cell, but also the determination of most important random inputs.
Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

No comments: