An Energy-Efficient Battery Monitoring and Logging System for Agricultural Robotics with CAN Bus Integration - Soosaar Guido; Lillerand Tormi (Environmental and Climate Technologies, 2025)
This article presents the design, implementation, and evaluation of a lightweight, energy-efficient battery monitoring and logging system tailored for agricultural robotics. Targeting off-grid field operations, the system features a modular architecture integrating a Controller Area Network (CAN) - based Battery Management System (BMS), a Raspberry Pi microcontroller, a low-power e-ink display for local feedback, and a web-accessible API for remote diagnostics. Core battery parameters – voltage, current, temperature, and state of charge – are collected via the CAN bus and logged to an onboard SQLite database. The system is fully configurable, lightweight, and modular, supporting compatibility with CAN-based BMS devices and open platforms like Robot Operating System (ROS). A key innovation is its adaptive data acquisition algorithm, which adjusts polling frequency based on battery activity and temperature thresholds, significantly reducing power consumption without compromising responsiveness. Beyond real-time monitoring, the system’s primary value lies in the structured dataset it generates. This long-term data enables future applications such as AI-based diagnostics, predictive maintenance, and adaptive control strategies. All operational thresholds and refresh rates are user-adjustable via the database, allowing precise tuning to field conditions. Preliminary tests confirm the system’s ability to detect anomalies, support historical diagnostics, and reduce energy consumption through intelligent scheduling. The hardware-agnostic and non-proprietary approach makes the platform scalable and adaptable to a wide range of CAN-compatible systems. By combining modular design, dynamic data logging, and remote access, the system advances sustainable battery management in agricultural robotics and creates a foundation for future integration with autonomous systems and machine learning models.