Developing an On-Board Diagnostic Tool for Estimating the State of Charge for Battery in Electric Vehicle

Precise State of Charge (SOC) estimation is crucial in maximizing the performance, safety, and longevity of electric vehicle (EV) batteries. Existing approaches are challenged by temperature changes, aging phenomena, and sensor bias, resulting in estimation errors. This paper introduces a new IoT-based on-board diagnostic system for real-time SOC estimation, combining high-accuracy voltage, current, and temperature sensors with an ESP32 microcontroller. The suggested system integrates Coulomb counting, open-circuit voltage (OCV), and adaptive filter methods to improve estimation precision while compensating for drift errors. Experimental testing shows that the system has an SOC estimation error margin of ±2%, where updates are realized in less than a second. This research makes a contribution to smart battery management systems that advance energy efficiency and dependability in EVs.