Show HN: EV-QA-Framework – ML-powered QA for electric vehicle battery systems
The EV-QA-Framework is a machine learning-powered quality assurance tool designed for electric vehicle battery systems. It enables QA engineers to detect anomalies, predict battery degradation, and validate performance without the need for costly test setups. The framework integrates various technologies, including machine learning algorithms and a CAN bus emulator, to enhance testing efficiency.
- ▪The framework utilizes Isolation Forest for anomaly detection and LSTM for State of Health prediction.
- ▪It features a real-time dashboard powered by FastAPI and WebSocket for interactive data visualization.
- ▪Users can generate synthetic CAN data streams for offline testing using the CAN bus emulator.
Opening excerpt (first ~120 words) tap to expand
EV-QA-Framework ML-Powered Quality Assurance Framework for Electric Vehicle Battery Systems Why Electric vehicle battery systems produce a lot of telemetry data - thousands of readings per minute. EV-QA-Framework helps QA engineers and battery researchers catch anomalies, predict degradation, and validate battery performance without needing expensive test rigs. The framework combines rule-based validation with machine learning (Isolation Forest for anomaly detection, LSTM for State of Health prediction), plus a CAN bus emulator so you can test without physical hardware.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.