REMAINING USEFUL LIFE PREDICTOR FOR EV BATTERIES USING MACHINE LEARNING

Electric vehicles (EVs) are a key solution to combat rising carbon emissions and reduce dependence on fossil fuels. In India, the government has implemented policies such as the Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) scheme to promote EV adoption Predicting the Remaining Useful Life (RUL) of EV batteries using machine learning ensures better battery health management and enhances operational efficiency. Applications include EV fleet management, battery recycling, and cost-effective maintenance. To develop a machine learning model that accurately predicts the Remaining Useful Life (RUL) of EV batteries to improve operational reliability, reduce maintenance costs, and support sustainable energy practices. Before the advent of machine learning, traditional methods for estimating EV battery life relied on rule-based approaches, where predefined thresholds such as voltage drops or charge cycles were used to predict battery health. Empirical models, often linear, were developed based on historical performance data but lacked the ability to adapt to dynamic usage patterns. Additionally, manual battery testing was a common practice to measure degradation, though it was time-consuming, labor-intensive, and often prone to inaccuracies in capturing the complex nature of battery aging. Traditional systems for predicting EV battery life are largely empirical and rely on static models that fail to capture dynamic battery behavior. These methods often lack precision, are labor-intensive, and provide limited adaptability to varying usage conditions, leading to inefficiencies in battery management. The increasing demand for EVs and their critical dependence on battery performance drives the need for accurate RUL prediction systems. Traditional methods are insufficient in addressing the complex, non-linear nature of battery degradation. The proposed machine learning-based system leverages real-time battery performance data, including metrics like voltage, current, temperature, and charge-discharge cycles, to train predictive models capable of estimating the Remaining Useful Life (RUL) of EV batteries. This approach significantly enhances accuracy by capturing complex patterns in battery degradation, enables real-time predictions for immediate insights, optimizes costs by minimizing unnecessary replacements and maximizing resource utilization, and promotes sustainability through efficient recycling and reduced battery waste.