AI-DRIVEN BATTERY HEALTH MONITORING AND PERFORMANCE ANALYSIS SYSTEM FOR ELECTRIC VEHICLES

Abstract

The rapid adoption of electric vehicles (EVs) has significantly increased the demand for intelligent battery management systems capable of improving battery performance, safety, and operational reliability. The lithium-ion battery pack is the most critical component of an electric vehicle because its performance directly affects driving range, charging efficiency, vehicle reliability, and overall operational cost. Conventional battery monitoring systems primarily measure voltage and current but often fail to accurately predict battery health degradation and remaining useful life under varying operating conditions. These limitations may result in unexpected battery failures, reduced battery lifespan, and increased maintenance costs. Recent advancements in Artificial Intelligence (AI), Internet of Things (IoT), embedded systems, cloud computing, and machine learning have enabled the development of intelligent Battery Health Monitoring Systems (BHMS) capable of continuously analysing battery parameters and predicting battery health in real time. This project presents an AI-Based Battery Health Monitoring System for Electric Vehicles. The proposed framework integrates an ESP32/Arduino controller, voltage sensor, current sensor, temperature sensor, AI-based battery health prediction model, IoT communication module, cloud platform, and mobile monitoring application into a unified intelligent battery monitoring system. The embedded controller continuously acquires battery voltage, current, temperature, State of Charge (SOC), and State of Health (SOH) data, which are analysed by the AI prediction model to identify battery degradation and abnormal operating conditions. Whenever unsafe battery conditions such as overheating, overcharging, deep discharge, or rapid degradation are detected, the system immediately generates warning alerts while transmitting battery information to the cloud platform for remote monitoring. Experimental evaluation demonstrates high prediction accuracy, reliable sensor monitoring, low response time, stable cloud communication, and accurate battery health estimation under different operating conditions. The proposed framework significantly improves battery safety, extends battery lifespan, reduces maintenance costs, enhances electric vehicle reliability, and provides a cost-effective solution for nextgeneration intelligent battery management systems.