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Paper WeA01.6

Joseph, Joshua (Massachusetts Institute of Technology), Doshi, Finale (MIT), Roy, Nicholas (Massachusetts Institute of Technology)

A Bayesian Nonparametric Approach to Modeling Battery Health

Scheduled for presentation during the Regular Session "Learning and Adaptation Control of Robotic Systems II" (WeA01), Wednesday, May 16, 2012, 09:45−10:00, Meeting Room 1 (Mini-sota)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 12, 2017

Keywords Learning and Adaptive Systems, Failure Detection and Recovery

Abstract

The batteries of many consumer products are often both a substantial portion of the item'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 iRobot Roomba batteries.

 

 

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