EV-QA-Framework: Open-Source ML-Powered Quality Analysis for EV Battery Systems

The open-source EV-QA-Framework provides a machine learning pipeline for analyzing electric vehicle battery data, including validation, anomaly detection, and state-of-health prediction. The framework supports BMS telemetry, Tesla Model S/X logs, and includes a CAN bus emulator for testing without hardware. Built with FastAPI, it offers real-time visualization of temperature, voltage, and SOC graphs with detected anomalies.

EV-QA-Framework https://github.com/remontsuri/EV-QA-Framework is an open-source machine learning framework for analyzing electric vehicle battery data. Whether you work with BMS telemetry, Tesla Model S/X logs, or just want to experiment with ML in the automotive domain — this tool covers the full pipeline from data ingestion to visualization. Raw CAN bus data is noisy. The validation module checks: python from ev qa framework import QAFramework qa = QAFramework report = qa.validate telemetry "battery data.csv" print report.summary Automatically finds outliers in battery telemetry — early indicators of cell degradation or defects: python from ev qa framework.analysis import BatteryAnomalyDetector detector = BatteryAnomalyDetector contamination=0.05 anomalies = detector.fit predict telemetry data detector.visualize The visualization highlights points where battery behavior deviates from normal patterns — critical for predictive diagnostics. Predicts State of Health remaining capacity from historical charge/discharge cycles: python from ev qa framework.soh predictor import SOHPredictor predictor = SOHPredictor seq length=50 predictor.train charging cycles predicted soh = predictor.predict current data print f"Predicted SOH: {predicted soh:.1f}%" The model supports export for embedded deployment. Test your pipeline without hardware — generates realistic battery telemetry: python from ev qa framework.can bus import CANEmulator from ev qa framework.config import TESLA MODEL S CONFIG emulator = CANEmulator config=TESLA MODEL S CONFIG for frame in emulator.stream frequency=10 : dashboard.update frame Supports custom load profiles via settings.yaml . Built with FastAPI — real-time telemetry visualization out of the box: docker compose up -d or directly: pip install ev-qa-framework python quickstart.py The dashboard shows temperature, voltage, SOC graphs, and detected anomalies. python pip install ev-qa-framework python -c " from ev qa framework import QAFramework qa = QAFramework qa.quickstart " Or with Docker: docker pull ghcr.io/remontsuri/ev-qa-framework:latest docker compose up MIT licensed. Contributions, issues, and discussions are welcome.