A Bayesian nonparametric approach to modeling battery health
Author(s)Doshi-Velez, Finale P.; Roy, Nicholas; Joseph, Joshua Mason
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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.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
Proceedings of the 2012 IEEE International Conference on Robotics and Automation
Institute of Electrical and Electronics Engineers (IEEE)
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.
Author's final manuscript