Research Papers


A Dynamic Ensemble for Estimating State-of-Charge of Interchangeable Robot Batteries

Contributing USMA Research Unit(s)

Electrical Engineering and Computer Science, Robotics Research Center

Document Type

Conference Proceeding

Publication Date

Fall 10-2017


This paper presents a unique machine learning model that estimates battery state-of-charge (SOC) for robotic applications. Unlike earlier approaches, this study investigates the problem of estimating SOC for several interchangeable batteries that can be used to power a robot. Robots commonly have a reserve pool of batteries available to be swapped for the purpose of extending operational time, but swapping batteries complicates the SOC estimation problem due to parameter variation. The proposed state-based ensemble is novel in that it exceeds the accuracy of traditional ensemble methods by dynamically changing estimation algorithms and predictors based on a preliminary (i.e., rough) state estimate of the battery. Experimental results show statistically significant improvement, on average, of 4 percent for our proposed state-based ensemble.

USMA Center/Institute Affiliation

Robotics Research Center

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