{"slug": "ev-qa-framework-open-source-ml-powered-quality-analysis-for-ev-battery-systems", "title": "EV-QA-Framework: Open-Source ML-Powered Quality Analysis for EV Battery Systems", "summary": "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.", "body_md": "[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.\n\nRaw CAN bus data is noisy. The validation module checks:\n\n``` python\nfrom ev_qa_framework import QAFramework\n\nqa = QAFramework()\nreport = qa.validate_telemetry(\"battery_data.csv\")\nprint(report.summary())\n```\n\nAutomatically finds outliers in battery telemetry — early indicators of cell degradation or defects:\n\n``` python\nfrom ev_qa_framework.analysis import BatteryAnomalyDetector\n\ndetector = BatteryAnomalyDetector(contamination=0.05)\nanomalies = detector.fit_predict(telemetry_data)\ndetector.visualize()\n```\n\nThe visualization highlights points where battery behavior deviates from normal patterns — critical for predictive diagnostics.\n\nPredicts State of Health (remaining capacity) from historical charge/discharge cycles:\n\n``` python\nfrom ev_qa_framework.soh_predictor import SOHPredictor\n\npredictor = SOHPredictor(seq_length=50)\npredictor.train(charging_cycles)\npredicted_soh = predictor.predict(current_data)\nprint(f\"Predicted SOH: {predicted_soh:.1f}%\")\n```\n\nThe model supports export for embedded deployment.\n\nTest your pipeline without hardware — generates realistic battery telemetry:\n\n``` python\nfrom ev_qa_framework.can_bus import CANEmulator\nfrom ev_qa_framework.config import TESLA_MODEL_S_CONFIG\n\nemulator = CANEmulator(config=TESLA_MODEL_S_CONFIG)\nfor frame in emulator.stream(frequency=10):\n    dashboard.update(frame)\n```\n\nSupports custom load profiles via `settings.yaml`\n\n.\n\nBuilt with FastAPI — real-time telemetry visualization out of the box:\n\n```\ndocker compose up -d\n# or directly:\npip install ev-qa-framework\npython quickstart.py\n```\n\nThe dashboard shows temperature, voltage, SOC graphs, and detected anomalies.\n\n``` python\npip install ev-qa-framework\npython -c \"\nfrom ev_qa_framework import QAFramework\nqa = QAFramework()\nqa.quickstart()\n\"\n```\n\nOr with Docker:\n\n```\ndocker pull ghcr.io/remontsuri/ev-qa-framework:latest\ndocker compose up\n```\n\nMIT licensed. Contributions, issues, and discussions are welcome.", "url": "https://wpnews.pro/news/ev-qa-framework-open-source-ml-powered-quality-analysis-for-ev-battery-systems", "canonical_source": "https://dev.to/remontsuri/ev-qa-framework-open-source-instrumient-dlia-ml-analiza-kachiestva-tiaghovykh-batariei-eliektromobiliei-1m9g", "published_at": "2026-05-29 08:54:10+00:00", "updated_at": "2026-05-29 09:12:06.517821+00:00", "lang": "en", "topics": ["machine-learning", "ai-tools", "ai-research", "ai-products", "ai-infrastructure"], "entities": ["EV-QA-Framework", "BatteryAnomalyDetector", "SOHPredictor", "CANEm", "Tesla"], "alternates": {"html": "https://wpnews.pro/news/ev-qa-framework-open-source-ml-powered-quality-analysis-for-ev-battery-systems", "markdown": "https://wpnews.pro/news/ev-qa-framework-open-source-ml-powered-quality-analysis-for-ev-battery-systems.md", "text": "https://wpnews.pro/news/ev-qa-framework-open-source-ml-powered-quality-analysis-for-ev-battery-systems.txt", "jsonld": "https://wpnews.pro/news/ev-qa-framework-open-source-ml-powered-quality-analysis-for-ev-battery-systems.jsonld"}}