Data-Driven Lead-Acid Battery Prognostics Using Random Survival Forests

Erik Frisk, Mattias Krysander, and Emil Larsson
Submission Type: 
Full Paper
phmc_14_012.pdf1.16 MBSeptember 2, 2014 - 7:08am

Problems with starter batteries in heavy-duty trucks can cause
costly unplanned stops along the road. Frequent battery changes
can increase availability but is expensive and sometimes not
necessary since battery degradation is highly dependent on the
particular vehicle usage and ambient conditions. The main
contribution of this work is a case-study where prognostic
information on remaining useful life of lead-acid batteries in
individual Scania heavy-duty trucks is computed. A data-driven
approach using random survival forests is proposed where the
prognostic algorithm has access to fleet management data including
291 variables from 33603 vehicles from 5 different European
markets. The data is a mix of numerical values such as temperatures
and pressures, together with histograms and categorical data such as
battery mount point. Implementation aspects are discussed such as
how to include histogram data and how to reduce the computational
complexity by reducing the number of variables. Finally, battery
lifetime predictions are computed and evaluated on recorded data
from Scania's fleet-management system.

Publication Year: 
Publication Volume: 
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Submission Keywords: 
Data Driven
random survival forests
heavy-duty trucks
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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