BL-Monitor: The Ultimate Guide to Features and SetupBL-Monitor is a versatile monitoring solution designed for tracking battery, system, and device-level metrics across a range of hardware platforms. Whether you’re a systems administrator, an embedded developer, or a power-aware user, this guide covers BL-Monitor’s core features, supported hardware, installation and setup, configuration best practices, troubleshooting, and optimization tips to get the most out of the tool.
What is BL-Monitor?
BL-Monitor is a monitoring tool focused primarily on battery life (BL) and related system telemetry. It aggregates and visualizes metrics such as battery voltage, current draw, state of charge (SoC), temperature, and charging cycles. In addition, BL-Monitor can collect system-level information—CPU/GPU usage, memory consumption, disk I/O, and network statistics—so you can correlate power events with system behavior.
Key use cases:
- Diagnosing battery drain issues on laptops, mobile devices, or embedded systems.
- Tracking long-term battery health and cycle counts.
- Correlating power usage spikes with software processes and hardware events.
- Embedding telemetry into products for R&D and quality assurance.
Core features
- Real-time telemetry streaming and historical logging.
- Multi-platform support (Linux, Windows, macOS, and various RTOS/embedded environments).
- Plugin architecture for custom sensors and data sources.
- Alerting and thresholds (email, webhooks, or dashboard notifications).
- Built-in visual dashboard with charts, tables, and exportable CSV logs.
- Scriptable API and command-line interface (CLI) for automation.
- Lightweight agents for resource-constrained devices.
What sets BL-Monitor apart: It emphasizes battery-centric metrics and integrates them with system telemetry to make power-debugging straightforward.
Supported hardware and sensors
BL-Monitor supports a wide range of battery management systems (BMS), smart batteries, and fuel gauge ICs (e.g., Texas Instruments, Maxim Integrated, Analog Devices), as well as standardized interfaces like SMBus and I2C fuel gauge protocols. For embedded devices, there are lightweight agent builds for ARM Cortex-M and other microcontrollers.
Commonly supported data points:
- Voltage (cell-level and pack-level)
- Current (charge/discharge)
- State of charge (SoC) and state of health (SoH)
- Temperature sensors (ambient and cell)
- Cycle count and full charge capacity
- Charger status and power path metrics
Installation
Below are general installation steps; specific commands vary by platform.
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Choose deployment type:
- Local desktop agent + dashboard
- Headless agent for servers/embedded devices sending data to a central server
- Docker container with bundled server and UI
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Install prerequisites:
- For server: Node.js or Python runtime, database (SQLite for small setups, PostgreSQL for larger)
- For agents: appropriate runtime or cross-compiled binary for target hardware
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Install the server:
- On Linux/macOS: download the tarball or use package manager (if available).
- On Windows: use MSI installer or the provided ZIP.
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Install agent on target device:
- Use platform-specific package or copy the binary.
- Configure permissions (access to I2C/SMBus, serial ports, or appropriate kernel interfaces).
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Start services:
- Start the database, server, and agent(s).
- Open the dashboard at the configured URL.
Basic configuration
- Configure agents with a YAML/JSON file specifying:
- Data sources (I2C addresses, serial ports, system files like /sys/class/power_supply)
- Sampling interval (e.g., 1s for real-time debugging, 60–300s for long-term logging)
- Network endpoint (server URL, authentication token)
- Local storage path and log rotation policy
Example agent configuration (conceptual):
agent: name: edge-device-01 sampling_interval_seconds: 5 data_sources: - type: smbus address: 0x0b driver: bq27441 - type: sysfs path: /sys/class/power_supply/BAT0 server: endpoint: https://bl-monitor.example.com/api/v1/ingest auth_token: "REDACTED" storage: max_days: 90 Dashboard and visualization
BL-Monitor’s dashboard provides:
- Live graphs for voltage, current, and SoC.
- Overlay of system events (process starts, CPU spikes) on power graphs.
- Customizable dashboards per device or device group.
- Export options: CSV, JSON, and image snapshots of charts.
- Aggregation views for fleet-level analytics (min/avg/max across devices).
Tips:
- Use short sampling intervals when you need fine-grained debugging, and longer intervals for trend analysis.
- Create dashboards for both raw sensor values and derived metrics (e.g., power = voltage × current).
Alerts and thresholds
Set thresholds for critical metrics:
- Low SoC (e.g., 10%): notify via email/webhook.
- High temperature (e.g., > 60°C): immediate alert.
- Sudden current spikes: trigger investigation.
BL-Monitor supports:
- Simple threshold alerts.
- Composite alerts (e.g., high temperature AND discharging).
- Notification integrations (Slack, PagerDuty, custom webhooks).
Integration and automation
- CLI and REST API to query historical data and push configuration.
- Webhooks for event-driven workflows (e.g., trigger a script to reduce CPU governor when battery is low).
- SDKs for Python and JavaScript to embed BL-Monitor data into other tools.
Example automation: reduce screen brightness and limit CPU frequency when SoC < 20% using the REST API and a small shell script.
Security considerations
- Use TLS for agent-to-server communication.
- Use per-agent authentication tokens and rotate periodically.
- Limit agent permissions on devices (only access required interfaces).
- Secure the dashboard with strong credentials and role-based access.
Troubleshooting
Common issues and fixes:
- No data from agent: check access to I2C/SMBus or serial ports and ensure the agent has permissions.
- Incorrect SoC readings: verify fuel gauge driver compatibility and calibration data (full charge capacity and design capacity).
- High resource usage by agent: increase sampling interval or enable local aggregation to reduce CPU/disk I/O.
Logs:
- Agent logs usually contain sensor read errors, network issues, and authentication failures. Check verbose mode for more details.
Optimization tips
- Calibrate your fuel gauge periodically using full charge/discharge cycles where safe.
- Use adaptive sampling: higher frequency during active debugging, lower for normal operation.
- Offload long-term storage to a central database and keep only recent high-resolution data on-device.
- Aggregate across devices to detect fleet-level anomalies (sudden drop in average SoH).
Example deployment scenarios
- Single laptop user: local agent + desktop dashboard to debug apps draining battery.
- Embedded product R&D: agents on prototype boards stream data to central server for analysis and regression testing.
- Fleet monitoring: headless agents on many devices reporting to cloud server with alerts for units showing rapid degradation.
Conclusion
BL-Monitor is a battery-focused telemetry system that combines sensor-level battery metrics with system telemetry to help diagnose, monitor, and optimize power usage across individual devices or fleets. Proper configuration, secure deployment, and sensible sampling strategies let you capture the right level of detail while managing resource usage.
If you want, tell me the platform you’ll deploy to (Windows, Linux laptop, Raspberry Pi, ARM microcontroller, etc.) and I’ll provide a tailored installation and configuration snippet.
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