Abstract:
Despite extensive progress in electric vehicle fault modelling, detection and classification
of anomalous behavior in unforeseen circumstances is still a huge challenge in battery safety. The
surety of billions of automobiles safety is still a question. Due to insufficient data and
unpredictability of the behavior of the batteries, deep learning methods have shown limited success
in anomaly detection and classification. Moreover, deep learning models tend to learn the
anomalies during training while on testing they try to fit in the anomalies that leads to false negative
instances. In recent times, unsupervised and semi-supervised learning techniques are in great
demand in this domain. Reinforcement Learning has previously been used in robotics and
autonomous driving for interaction, decision making, manipulation and control. Reinforcement
Learning can self-learn the internal distribution of the normal and abnormal data and create
anomalous instances. In this research, an algorithm is proposed for detecting and classifying
anomaly in EV Battery packs using Deep Q-Network and its variant. Synthetic dataset is generated
to train the model and testing has been done on lab generated data to ensure the robustness of the
proposed model.
