Paper Title
Estimation of Safe Operating Area for Electric Vehicle Battery

Abstract
In recent times, electric vehicles (EVs) have experienced battery pack failures due to various factors such as short circuits, thermal imbalances, fires, and explosions. This research paper employs a machine-learning approach to estimate lithium- ion batteries’ safe operating area (SOA). It explores the factors that influence battery safety levels and discusses the safe operating area for charging and discharging based on real-time data. The Safe Operating Area (SOA) concept is a critical consideration in the design, operation, and management of EV batteries, as it defines the limits within which the battery can operate safely without compromising performance or endangering the vehicle. The significance of SOA analysis for EV batteries and its implications on battery design, thermal management, charging strategies, and overall vehicle safety. This paper specifically addresses the estimation of the safe operation of a battery using ML in relation to voltage and temperature with respect to time. It emphasises the importance of understanding the interplay between these parameters to establish safe operational limits. Keywords - Artificial Intelligence (AI), Battery Management System (BMS), Electric Vehicle (EV), Machine Learning (ML), State of Function (SOF), Safe Operating Area (SOA), State of Health (SOH).