Predictive maintenance aims to predict failures in components
of a system, a heavy-duty vehicle in this work, and do maintenance
before any actual fault occurs. Predictive maintenance
is increasingly important in the automotive industry due to the
development of new services and autonomous vehicles with
no driver who can notice first signs of a component 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 electrical 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 Memory (LSTM) neural networks
and one on Random Survival Forest (RSF). The objective is
to estimate time of battery failure 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 measurements are directly related to
battery health, 2) the number of data readouts vary from one
vehicle to another, and 3) readouts 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 dis-
cussed and how the amount of vehicle information influences
performance.