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To access ground truth degradation information, we simulatedcharge and discharge cycles of automotive lithium ion batteriesin their healthy and degrading states and used this informationto determine performance of an autoencoder-basedanomaly detector. The simulated degradation mechanism wasan abrupt increase in the battery’s rate of time-dependent capacityfade. The neural network topology was based on onedimensionalconvolutional layers. The decision-support system,based on the sequential probability ratio test, interpretedthe anomaly generated by the autoencoder. Detection timeand time to failure were the metrics used for performanceevaluation. Anomaly detection was evaluated on five differentsimulated progressions of damage to examine the effectsof driving profile randomness on performance of the anomalydetector.

Evaluation of 1D CNN Autoencoders for Lithium-ion Battery Condition Assessment Using Synthetic Data