Cloud-Enhanced Battery Management System Architecture for Real-Time Data Visualization, Decision Making, and Long-Term Storage

The rapid advancement of battery management systems (BMS) in automotive applications demands real-time, automated data acquisition and visualization architectures capable of handling complex battery dynamics. This paper introduces a robust, scalable cloud-based architecture that seamlessly integrates with the physical on-board BMS for enhanced monitoring, predictive analytics, and long-term data storage. The system uses automotive-grade hardware, including an NXP BMS and STM32 microcontroller and an efficient Python-based CAN data decoding algorithm to enable accurate real-time monitoring and visualization via Grafana®. Comprehensive experiments reveal the system's efficiency in tracking critical parameters like cell voltage, temperature, and balancing voltage, ensuring proactive detection of weak and faulty cells, thereby improving battery safety. Key contributions include high-resolution, precision real-time battery data sampling; efficient CAN data decoding; data safety and security; identification of weak cells; and analysis of how data sampling rates and cloud server locations impact communication latency, memory usage, and computational power. Understanding these factors is crucial for the scalability of cloud-based BMS in automotive applications. The proposed architecture will aid in the practical implementation of cloud-enhanced BMS and digital twin-based BMS. It will also benefit second-life applications of retired automotive batteries due to long-term historical data storage.