Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data
Predictive maintenance aims to predict failures in compo- nents of a
system, a heavy-duty vehicle in this work, and do maintenance before
any actual fault occurs. Predictive main- tenance is increasingly
important in the automotive industry due to the development of new
services and autonomous ve- hicles with no driver who can notice first
signs of a compo- nent problem. The lead-acid battery in a heavy
vehicle is mostly used during engine starts, but also for heating and
cooling the cockpit, and is an important part of the electri- cal
system that is essential for reliable operation. This paper develops
and evaluates two machine-learning based methods for battery
prognostics, one based on Long Short-Term Mem- ory (LSTM) neural
networks and one on Random Survival Forest (RSF). The objective is to
estimate time of battery fail- ure based on sparse and non-equidistant
vehicle operational data, obtained from workshop visits or
over-the-air readouts. The dataset has three characteristics: 1) no
sensor measure- ments are directly related to battery health, 2) the
number of data readouts vary from one vehicle to another, and 3) read-
outs are collected at different time periods. Missing data is common
and is addressed by comparing different imputation techniques. RSF-
and LSTM-based models are proposed and evaluated for the case of
sparse multiple-readouts. How to measure model performance is
discussed and how the amount of vehicle information influences
performance.
Sergii Voronov, Erik Frisk and Mattias Krysander
International Journal of Prognostics and Health Management,
Accepted for publication
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