Early Detection of Rare Events: Predicting Battery Cell Deviations
Ladda ner
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2023
Författare
Boberg, Jesper
Segerlund, Anders
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Despite rigorous quality controls in battery cell production, the production process is
still subject to quality deviations. These quality deviations; known as "rare events",
initially act as passive quality deviations which may not affect the battery’s perfor mance. However, a passive quality deviation can transition into an active deviation
that may give rise to behavioral issues in the battery cell at some point during its
lifetime. An active quality deviation can cause the entire battery to misbehave and
eventually fail. This thesis investigates the possibility of predicting these cell devia tions in car batteries. Better predictions of these events would avoid expensive and
troublesome car failures and enable preventive car maintenance to solve the problem.
In this report, different models are created and evaluated with the aim of preventing
these deviations. The dataset is supplied by Volvo Cars and contains a large amount
of data collected from Battery Electric Vehicles (BEVs), where the arguably largest
challenge comes from the imbalance of the dataset. In addition to the modelling,
the thesis includes a thorough data analysis with the aim of improving both the
dataset itself and the data collection process at Volvo Cars.
These deviations occur extremely rarely, making a relatively large amount of false
positives difficult to avoid. The results show that a simple time series model can
catch these deviations relatively well but also brings along a large number of false
positives. A neural network is able to improve this significantly, still being able to
catch the majority of the deviations while producing a lot fewer false positives.
Beskrivning
Ämne/nyckelord
Battery failures, Cell Deviation, Recurrent Neural Network, Time Series Analysis, Machine Learning, Multilayer Perceptron, Predictive Modelling, Physics, Car Batteries, Volvo Cars.