AI driven real-time battery SOC estimation with intelligent power control
TL;DR
This paper presents an integrated hardware–software framework for real-time SOC estimation and intelligent power control using a machine learning approach and demonstrates high prediction accuracy with a mean absolute error of 0.0062% and R2 of 99.9997%, outperforming conventional and deep learning-based approaches.
Abstract
Accurate estimation of battery state of charge (SOC) is essential for the reliable and efficient operation of battery management systems in electric vehicles, renewable energy storage, and portable electronics. Conventional techniques such as open-circuit voltage measurement and Coulomb counting suffer from limitations including sensitivity to operating conditions, cumulative errors, and reduced accuracy under dynamic loads. This paper presents an integrated hardware–software framework for real-time SOC estimation and intelligent power control using a machine learning approach. An Arduino-based sensing layer acquires voltage, current, temperature, and discharge time, which are processed using a hyperparameter-optimized random forest regression model implemented in Python. The predicted SOC is transmitted to an ESP32 microcontroller that executes a threshold-based load management strategy using optocoupler-isolated relay switching. Experimental results demonstrate high prediction accuracy with a mean absolute error of 0.0062% and R2 of 99.9997%, outperforming conventional and deep learning-based approaches. The proposed system offers a low-cost, scalable, and internet of things-enabled solution for precise battery monitoring and adaptive load control in real-time applications.