Data-driven Lead-Acide Battery Prognostics Using Random Survival Forests
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 33 603 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.
Erik Frisk, Mattias Krysander and Emil Larsson
2014
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