IoT-Based Smart Battery Management System for Electric Vehicles: Real-Time Monitoring, AI-Driven Predictive Analytics, and Cloud Integration

Abstract

The global shift towards electric vehicles (EVs) necessitates the advancement of intelligent battery management technologies. This paper presents an IoT-based Smart Battery Management System (SBMS) that combines real-time monitoring, AI-driven predictive analytics, and seamless cloud integration to improve EV battery performance, safety, and lifecycle. Traditional BMS architectures are limited by static monitoring and lack predictive capabilities. The proposed SBMS overcomes these limitations through a three-layered architecture: IoT-based sensing, AI-powered analytics, and cloud infrastructure. Real-time data such as voltage, current, temperature, and state of charge (SoC) are collected via embedded sensors and processed using machine learning models to predict state of health (SoH) and detect anomalies. Cloud platforms provide remote diagnostics, centralized storage, OTA updates, and fleet management tools. Experimental results from a prototype validate the system’s accuracy, responsiveness, and predictive maintenance capabilities, offering a scalable and future-ready solution for the EV industry.