Data-driven lead-acid battery lifetime prognostics
To efficiently transport goods by heavy-duty trucks, it is important
that vehicles have a high degree of availability and in particular
avoid becoming standing by the road unable to continue the transport
mission. An unplanned stop by the road does not only cost due to the
delay in delivery, but can also lead to a damaged cargo. %
%Therefore,
%maintenance planning becomes important in the automotive industry and
%in the near future car or truck manufactures do not only produce and
%deliver cars and trucks, but also provide maintenance services that
%will allow fleet owners or regular customers to avoid unexpected
%failures.
High availability can be achieved by changing components
frequently, but such an approach is expensive both due to the frequent
visits to a workshop and also due to the component cost. Therefore,
failure prognostics and flexible maintenance has significant potential
in the automotive field for both manufacturers, commercial fleet
owners, and private customers.
In heavy-duty trucks, one cause of unplanned stops are failures in the
electrical power system, and in particular the lead-acid starter
battery. The main purpose of the battery is to power the starter motor
to get the diesel engine running, but it is also used to, for example,
power auxiliary units such as cabin heating and kitchen
equipment. Detailed physical models of battery degradation is
inherently difficult and requires, in addition to battery health
sensing which is not available in the given study, detailed knowledge
of battery chemistry and how degradation depends on the vehicle and
battery usage profiles.
The main aim of the given work is to predict the lifetime of lead-acid batteries using data-driven approaches. Main contributions in the thesis are: a) the choice of the Random Survival Forest method as the model for predicting a conditional reliability function which is used as the estimator of the battery lifetime, b) variable selection for better predictability of the model and c) variance estimation for the Random Survival Forest method.
%There are large amounts of data available, logged from trucks
%in operation. However, data is not closely related to battery health
%which makes battery prognostic challenging.
When developing a
data-driven prognostic model and the number of available variables
is large, variable selection is an important task, since including
non-informative variables in the model have a negative impact on
prognosis performance. Two features of the dataset has been
identified, 1) there are few informative variables, and 2) highly correlated
variables in the dataset. The main contribution is a novel method
for identifying important variables, taking these two properties
into account, using Random Survival Forests to estimate prognostics
models. The result of the proposed method is compared to existing
variable selection methods, and applied to a real-world automotive
dataset.
Confidence bands are introduced to the RSF model giving an opportunity for an engineer to observe the confidence of the model prediction. Some aspects of the confidence bands are considered: a) their asymptotic behavior and b) usefulness in the model selection. A problem of including time related variables is addressed in the thesis with arguments why it is a good choice not to add them into the model. Metrics for performance evaluation are suggested which show that the model can be used to find and optimize cost of the battery replacement.
Sergii Voronov
2017
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Senast uppdaterad: 2021-11-10
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