To access ground truth degradation information, we simulated
charge and discharge cycles of automotive lithium ion batteries
in their healthy and degrading states and used this information
to determine performance of an autoencoder-based
anomaly detector. The simulated degradation mechanism was
an abrupt increase in the battery’s rate of time-dependent capacity
fade. The neural network topology was based on onedimensional
convolutional layers. The decision-support system,
based on the sequential probability ratio test, interpreted
the anomaly generated by the autoencoder. Detection time
and time to failure were the metrics used for performance
evaluation. Anomaly detection was evaluated on five different
simulated progressions of damage to examine the effects
of driving profile randomness on performance of the anomaly
How to Cite
Battery, Deep learning, Simulation, 1D Convolutional Neural Networks
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