Predictive System for Early Failure Detection in EV Battery Bank Cells

This research paper presents a predictive system for early detection of battery failure in Electric Vehicle (EV) battery banks, enhancing reliability, safety, and efficiency. Leveraging advanced analytics, machine learning algorithms, and real-time monitoring, this system identifies potential battery faults and provides timely alerts. Experimental data collection, predictive modelling, and simulation testing are employed to validate the approach. To reliably forecast battery failure, the suggested system incorporates numerous data sources, such as temperature, charge/discharge rates, and cell voltage. The outcomes show increased EV performance, lower maintenance costs, and better battery reliability. This creative technique ensures peak battery performance and reduces downtime, promoting the broad adoption of EVs.