Applying BERT for feature Extraction in battery management system domain: A named entity recognition perspective

Optimizing battery performance, enhancing energy storage systems, and assuring their safe and reliable operation all depend heavily on the analysis of battery data. The widely used BERT model is examined in this study with a focus on the NER task for feature extraction in battery data. By utilizing BERT, it will be possible to identify and categorize important battery-related assets such safety terminology, events, conditions and digital signal state. A approach is suggested that involves fine-tuning BERT in a battery-specific corpus, taking advantage of the capacity to learn representations from large-scale text data. It is aimed to demonstrate the effectiveness of the methodology in accurately extracting battery-related assets through the application of experiments and evaluations.The study aims to make a significant contribution to accelerating and automating RE document review by providing a more complex and contextually aware approach to feature extraction through the use of BERT-based architectures in projects developed in the BMS field.