To make the test automation process go faster, we needed to harness innovation. So how do we do this? So the first strategy was to figure out how we could leverage AI to speed up the test development cycle. The second strategy involved developing a test automation care package.

This paper presents a cloud-edge digital twin frame- work designed to enhance battery lifecycle management within electric vehicles, contributing to sustainable transportation and advanced battery system engineering. The architecture integrates a static state-of-health (SOH) model trained offline with a dynamically retrained state-of-charge (SOC) model updated peri- odically via cloud-based machine learning.

This paper introduces a data-driven anomaly detection framework designed to enhance the safety and reliability of lithium-ion battery packs deployed in large-scale electric transport systems. Leveraging principal component analysis (PCA) and cumulative sum (CUSUM) control charts, the method monitors mean-based residuals of voltage and temperature across cell groups to detect early-stage faults such as internal short circuits, sensor failures, and thermal irregularities.