Battery failures cost the EV industry $5 billion annually in warranty claims and safety incidents. Manual testing can't scale to monitor millions of telemetry data points from voltage, temperature, and State of Charge. This gap leaves critical failure modes like thermal runaway undetected until it's too late.

EV-QA-Framework is an open-source Python library addressing this directly. It runs 64+ automated tests for BMS validation, using scikit-learn's IsolationForest to detect subtle anomalies like voltage drops or SoC drift. Built with Python 3.12 and Pydantic v2, it prioritizes performance for hardware-in-the-loop testing.

The framework generates interactive Jupyter Notebook reports, helping engineers visualize failure patterns. While mainstream tools target web apps, this project focuses on hardware-specific edge cases. It's a practical tool for QA engineers and hobbyists aiming to build safer battery systems for EVs and e-bikes.