How It Works
Naubat collects structured diagnostic data from the vehicle through a Bluetooth Low Energy connection. The mobile app acts as a data gateway between the vehicle and the cloud platform, where signal processing and metric generation are performed.Step 1 — Connection
A Bluetooth Low Energy diagnostic dongle is connected to the vehicle interface. The mobile application performs secure pairing with the dongle. After pairing is complete, the app establishes a communication session for diagnostic exchange.Step 2 — Structured Data Requests
The app communicates with vehicle control units using the standardized UDS protocol. Requests are sent at a controlled cadence. Relevant vehicle control units return structured data responses. Sampling frequency is intentionally kept well below typical CAN bus operating loads, while remaining sufficient to capture the signal quality needed for later battery-state analysis.Step 3 — Signal Processing
Collected signals are transmitted to the cloud for processing. Raw signals are normalized into consistent units and formats. Data integrity checks are applied to detect incomplete, inconsistent, or out-of-range values. Invalid samples are filtered before metric computation.Step 4 — Metric Generation
Metrics are generated in the backend from validated processed signals. Trip and energy indicators are derived from this processed dataset. Battery-related analytics are computed server-side from structured historical and session data.Conceptual Data Flow
1) Connection Flow
Mobile App <-> BLE Dongle <-> Vehicle ECUs
Mobile App -> Backend
The mobile app maintains the bidirectional diagnostic link with the dongle and ECUs, and separately maintains upstream connectivity to the backend platform.
2) Data Flow
Mobile App -> UDS requests -> Vehicle ECUs
Vehicle ECUs -> UDS responses -> Mobile App
Mobile App -> Backend ingestion -> Signal translation to readable values -> Advanced metric processing
Raw diagnostic responses are sent to the backend, where they are translated into human-readable values and then used to compute higher-level analytics and metrics.