Integration of 5G and 4G Communication in Battery Management Systems for Electric Vehicles: A Cloud‐Based Architecture for Enhanced Performance and Analytics

The Cloud‐Based Architecture is proposed for the Integration of 4G and 5G Communication in a Battery Management System (BMS) for Electric Vehicles (EV). This study compares Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and an AI‐optimized BMS algorithm. The AI‐optimized BMS has recorded 88% State of Health (SoH), with a good old traditional BMS only managing 72%. Furthermore, the AI model reaches 85% energy efficiency, 20 ms latency, and 92% fault detection accuracy, surpassing existing approaches. Using Network performance analysis, 5G has 2.5× more throughput, and less latency (approx. 60% less than 4G), empowering real‐time monitoring. This can make Over‐the‐air (OTA) updates 98% reliable with 5G and 85% with 4G, ensuring the software updates success rate. Incorporating this AI‐based BMS system with 5G provides efficient automation of the battery management process, improving battery lifespan, energy efficiency, and enabling fault detection, predictive analysis, and remote battery update. Ideal for next‐gen EV implementations, this scalable and cloud‐based edge solution extends performance with low operational expenditure and optimal battery lifecycle management.