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

> Source: <https://dev.to/remontsuri/ev-qa-framework-open-source-instrumient-dlia-ml-analiza-kachiestva-tiaghovykh-batariei-eliektromobiliei-1m9g>
> Published: 2026-05-29 08:54:10+00:00

[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.