Treatment of accumulative variables in data-driven prognostics of lead-acid batteries
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
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 used
where the prognostic algorithm has access to fleet operational data
including 291 variables from $33 603$ vehicles from 5 different
European markets. A main implementation aspect that is discussed is
the treatment of accumulative variables such as vehicle age in the
approach. Battery lifetime predictions are computed and evaluated on
recorded data from Scania's fleet-management system and the effect of
how accumulative variables are handled is analyzed.
Erik Frisk and Mattias Krysander
2015
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