🔋 EV-QA-Framework

Open-source Python framework for Electric Vehicle battery quality assurance and anomaly detection

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🚀 Why This Matters for EV Industry

Battery failures cost the EV industry $5B+ annually in warranty claims, recalls, and safety incidents. Modern electric vehicles generate millions of telemetry points daily from battery management systems, but manual QA can’t scale.

This framework provides enterprise-grade testing tools to the open-source community:

• ✅ Automated quality assurance for battery telemetry (voltage, current, temperature, SOC)
• ✅ ML-powered anomaly detection using Isolation Forest (200 estimators, scikit-learn)
• ✅ 64+ comprehensive tests covering safety boundaries and edge cases
• ✅ Pydantic data validation ensuring data integrity
• ✅ CI/CD ready with Docker and GitLab CI

Target Audience: QA engineers at Tesla, Rivian, Lucid Motors, BYD, and automotive suppliers working on BMS (Battery Management Systems).

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📊 Real-World Problem

Battery failures cost the EV industry billions annually. Early detection of anomalies in telemetry can:

• 🔥 Prevent thermal runaway events
• 📉 Reduce warranty claims
• ⚡ Extend battery lifespan
• 🛡️ Improve vehicle safety

This framework automates detection of:

• Temperature spikes (>5°C jumps)
• Voltage anomalies (out of 3.0-4.3V range)
• Invalid SOC readings
• ML-detected outliers in multidimensional space

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🏗️ Architecture

┌─────────────────────────────────────────────┐
│         EV Battery Telemetry Data           │
│    (CAN bus / OBD-II / Cloud API)          │
└──────────────────┬──────────────────────────┘
│
▼
┌─────────────────────────────────────────────┐
│       Pydantic Validation Layer             │
│  (VIN, Voltage, Current, Temp, SOC, SOH)   │
└──────────────────┬──────────────────────────┘
│
┌──────────┴──────────┐
▼                     ▼
┌──────────────┐    ┌─────────────────┐
│ Rule-based   │    │  ML Anomaly     │
│ Validation   │    │  Detection      │
│ (Thresholds) │    │ (IsolationForest)│
└──────┬───────┘    └────────┬────────┘
│                     │
└──────────┬──────────┘
▼
┌──────────────────┐
│  Test Results    │
│  + Anomaly Report│
└──────────────────┘

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🆚 Comparison with Existing Tools

Feature	EV-QA-Framework	Battery-Emulator	BatteryML	BATLab
ML Anomaly Detection	✅ Isolation Forest	❌ Rule-based only	✅ Research models	❌ Manual analysis
Real-time Telemetry	✅ Pytest automation	✅ CAN bus	❌ Offline datasets	✅ Serial monitor
CI/CD Integration	✅ Docker/GitLab	❌	❌	❌
License	MIT (Commercial OK)	GPL-3.0	MIT	Proprietary
Language	Python	C++	Python	C#
Test Coverage	64+ automated tests	Hardware integration	Dataset analysis	Manual 10hr tests
Production Ready	✅ Docker + Pydantic	⚠️ Hardware-dependent	❌ Research only	⚠️ Windows-only

Our Competitive Advantages:

• 🧠 ML-first approach — catches unknown anomalies traditional rules miss
• 🐍 Python ecosystem — integrates with pandas, NumPy, scikit-learn
• 🔒 Type safety — Pydantic models prevent data corruption
• 🚀 Modern DevOps — GitLab CI, Docker, pytest

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🔧 Quick Start

Installation

git clone https://github.com/remontsuri/EV-QA-Framework.git
cd EV-QA-Framework
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

Run Tests

# All tests (64+)
pytest -v

# With coverage report
pytest --cov=. --cov-report=html

Usage Example

from ev_qa_models import validate_telemetry
from ev_qa_analysis import AnomalyDetector
import pandas as pd

# 1. Validate incoming telemetry
data = {
"vin": "1HGBH41JXMN109186",
"voltage": 396.5,
"current": 125.3,
"temperature": 35.2,
"soc": 78.5,
"soh": 96.2
}
telemetry = validate_telemetry(data)  # Pydantic auto-validation

# 2. ML Anomaly Detection
detector = AnomalyDetector(contamination=0.01, n_estimators=200)
detector.train(normal_telemetry_df)  # Train on historical "good" data

# Real-time detection
predictions, scores = detector.detect(new_telemetry_df)
# predictions: 1 = normal, -1 = anomaly

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📈 Test Coverage

Category	Tests	Coverage
Boundary Tests (Voltage/Temp/SOC)	23+	Temperature: >60°C, Voltage: 3.0-4.3V, SOC: 0-100%
Anomaly Detection	15+	Temperature jumps, multiple anomalies
ML Analyzer	12+	Isolation Forest, severity classification
Pydantic Models	14+	VIN validation, type checking, edge cases
TOTAL	64+	~85% code coverage

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🧪 Technologies

• Python 3.8+
• pytest (testing framework)
• Pydantic (data validation)
• scikit-learn (Isolation Forest ML)
• pandas/numpy (data processing)
• Docker (containerization)
• GitLab CI/CD (automation)

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🌟 Key Features

1. Pydantic Data Models

class BatteryTelemetryModel(BaseModel):
vin: str = Field(min_length=17, max_length=17)
voltage: float = Field(ge=0.0, le=1000.0)
temperature: float = Field(ge=-50.0, le=150.0)
soc: float = Field(ge=0.0, le=100.0)
soh: float = Field(ge=0.0, le=100.0)
# ... with automatic validation

2. ML Anomaly Detection

• Isolation Forest with 200 estimators
• Separates train() and detect() for production use
• Severity classification: CRITICAL / WARNING / INFO
• Detailed Russian documentation explaining algorithm

3. Comprehensive Testing

• Parametrized tests for boundary values
• Negative test cases (invalid types, extreme values)
• Async test suite support
• Docker-based CI/CD pipeline

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🐳 Docker Support

# Build
docker build -t ev-qa-framework .

# Run tests in container
docker run --rm ev-qa-framework pytest -v

# Docker Compose
docker-compose up --build

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🤝 Contributing

We welcome contributions from the EV and QA community!

Areas for collaboration:

• Integration with real CAN bus data (python-can)
• MQTT/OBD-II protocol support
• Integration with Tesla API / other EV APIs
• Enhanced ML models (LSTM for time-series)
• Web dashboard for real-time monitoring

See CONTRIBUTING.md for guidelines.

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📝 Industry Contact

For EV manufacturers, BMS suppliers, or automotive QA teams interested in collaboration:

This is an open-source educational project, but we’re open to:

• Enterprise consulting on battery QA systems
• Custom ML models for specific battery chemistries
• Integration with proprietary BMS systems
• Training workshops for QA teams

📧 Contact: [Your Email] 💼 LinkedIn: [Your Profile] 🐙 GitHub: @remontsuri

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📄 License

MIT License - see LICENSE for details.

Free for commercial use, including Tesla, Rivian, Lucid, BYD, and other EV manufacturers.

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🙏 Acknowledgments

Inspired by real-world challenges in EV battery safety and quality assurance. Built with ❤️ for the sustainable transportation revolution.

If this helps your EV project, consider:

• ⭐ Starring this repo
• 🍴 Forking and contributing
• 📢 Sharing with your QA/automotive network
• 💼 Hiring the author for EV QA roles 😊

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🔗 Related Projects

• python-can - CAN bus library
• Tesla API - Unofficial Tesla API
• Battery-Optimization - Battery modeling

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Built for the future of electric mobility 🌍⚡🚗