MACHINE LEARNING BASED PREDICTION OF REMAINING USEFUL LIFE IN EV BATTERIES

One important way to fight increasing carbon emissions and lessen reliance on fossil fuels is by using electric cars, or EVs. To encourage EV adoption, the Indian government has put regulations in place including the Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) initiative. Machine learning-based EV battery Remaining Useful Life (RUL) prediction improves operating efficiency and guarantees improved battery health management. EV fleet management, battery recycling, and economical maintenance are a few examples of applications. To enhance operational dependability, save maintenance costs, and promote sustainable energy habits by creating a machine learning model that reliably forecasts the Remaining Useful Life (RUL) of EV batteries. Prior to the development of machine learning, conventional techniques for predicting the lifespan of EV batteries were based on rule-based methodologies, in which battery health was predicted by predetermined criteria like voltage decreases or charge cycles. Frequently linear, empirical models were created using past performance data but were unable to adjust to changing use patterns. Furthermore, manual battery testing was a popular method of assessing deterioration, despite the fact that it was labour-intensive, time-consuming, and often prone to errors in capturing the intricate nature of battery ageing. The majority of traditional EV battery life prediction methods are empirical in nature and use static models that are unable to account for dynamic battery behaviour. These techniques often result in inefficient battery management since they are labour-intensive, imprecise, and provide no flexibility to changing use circumstances. The necessity for precise RUL prediction systems is fuelled by the growing demand for EVs and their crucial reliance on battery performance. Because battery deterioration is complicated and non-linear, traditional approaches are inadequate. The suggested machine learning-based method trains predictive models that can estimate the Remaining Useful Life (RUL) of EV batteries using real-time battery performance data, such as voltage, current, temperature, and charge-discharge cycles. This method greatly improves accuracy by identifying intricate patterns in battery deterioration, permits real-time forecasting for instant insights, minimises needless replacements and maximises resource use, and fosters sustainability through effective recycling and decreased battery waste.