IoT-Based Battery Health Monitoring for Electric Vehicles Using Machine Learning - Jay Bahadur Singh; Ramniwas Yadav (International Journal of Machine Systems and Manufacturing Technology, 2026)
With increasing utilization of the Electric Vehicles (EV)s in global scale, battery health management becomes a critical factor which has great impact on vehicle performance, safety and longevity. Battery materials, such as NMC LFP lithium-ion batteries and lithium-ion batteries, degrade over time from charging behaviour, heat stress, discharging voltage profiles and environmental limits. Conventional BMS only offer threshold based health diagnostics and cannot perform accurate degradation prediction. This work presents an IoT based Condition Monitoring System on Batteries with Machine Learning-based real-time estimation of the battery’s SoC, SoH, capacity fade and internal resistance growth as well as predicting the RUL. Real-time battery pack sensor data is sent off the edge to cloud platforms like Google Cloud IoT and AWS IoT Core using thin, IoT arms races protocols for ML-based health prediction. The experimental results show that the introduced system outperforms conventional BMS predictions for degradation state patterns indicating that IoT–ML can improve the reliability, safety and performance of EV.
Keywords: ESP32 microcontroller. Support Vector Regression; IoT–ML framework; battery management systems; electric vehicles