EDGE AI-BASED REAL-TIME BATTERY MANAGEMENT SYSTEMS: TECHNOLOGY TRENDS AND DESIGN FRAMEWORK

Battery management systems (BMSs) increasingly require low-latency, reliable, and intelligent processing capabilities to support the rapid expansion of electric vehicles and stationary energy storage systems. Conventional centralized and cloud-dependent BMS architectures can provide advanced analytics but may face limitations associated with communication latency, network dependence, data transmission overhead, and privacy. Edge artificial intelligence (Edge AI) provides an alternative approach by performing data processing and intelligent inference close to the battery system. This review examines major technology trends in real-time BMSs and proposes a reference design framework for Edge AI-based battery management. Recent developments in battery sensing, artificial intelligence-based state estimation, decentralized computing, lightweight machine learning, digital twins, communication, and cybersecurity are reviewed. Based on these trends, a layered framework is proposed in which battery sensors and embedded controllers acquire operating data, edge processors perform preprocessing and intelligent analysis, and cloud platforms provide long-term data management, model updating, and fleet-level analytics. The framework emphasizes local state estimation, anomaly detection, predictive maintenance, secure edge-cloud communication, and optional digital-twin integration. The review indicates that Edge AI can reduce dependence on continuous cloud connectivity and enable faster decision-making, although practical deployment remains constrained by limited embedded resources, model complexity, cybersecurity risks, heterogeneous battery platforms, and interoperability issues. Future BMS development is expected to combine lightweight AI, adaptive edge-cloud collaboration, explainable decision support, digital twins, and security-by-design principles.