A Bayesian nonparametric approach to modeling battery health
Name
Roy_A bayesian nonparametric.pdf
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543.78 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Doshi-Velez, Finale P.
Roy, Nicholas
Joseph, Joshua Mason
Date Issued
May 2012
Journal
Proceedings of the 2012 IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Joseph, Joshua, Finale Doshi-Velez, and Nicholas Roy. “A Bayesian nonparametric approach to modeling battery health.” In 2012 IEEE International Conference on Robotics and Automation, 1876-1882. Institute of Electrical and Electronics Engineers, 2012.
Version
Author's final manuscript
Abstract
The batteries of many consumer products are both a substantial portion of the product's cost and commonly a first point of failure. Accurately predicting remaining battery life can lower costs by reducing unnecessary battery replacements. Unfortunately, battery dynamics are extremely complex, and we often lack the domain knowledge required to construct a model by hand. In this work, we take a data-driven approach and aim to learn a model of battery time-to-death from training data. Using a Dirichlet process prior over mixture weights, we learn an infinite mixture model for battery health. The Bayesian aspect of our model helps to avoid over-fitting while the nonparametric nature of the model allows the data to control the size of the model, preventing under-fitting. We demonstrate our model's effectiveness by making time-to-death predictions using real data from nickel-metal hydride battery packs.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2012.6225178
