Battery Degradation Prediction for SOC & SOH Estimation of Lithium-Ion Battery in Electric Vehicle

Lithium-ion (Li-ion) batteries are essential in today’s electric vehicle (EV) production because they offer high energy storage and are lightweight. However, their chemical nature makes them unstable, which creates challenges for Battery Management Systems (BMS), especially when it comes to accurately measuring how much charge is left (State of Charge, or SOC) and how well the battery is functioning (State of Health, or SOH). As batteries age, caused by things like temperature changes, fast charging, and different levels of discharge, their ability to hold charge and deliver power decreases, making it harder to predict how long they will last. This paper reviews various methods used to estimate SOC and SOH, pointing out their strengths and weaknesses along with the errors they produce. We also suggest a smart monitoring system that uses data analysis and machine learning to predict how batteries will degrade and how much life they have left (Remaining Useful Life, or RUL). By combining real-world driving data with lab tests on battery aging, our model creates detailed degradation profiles. This research improves the safety, dependability, and affordability of electric vehicles, and offers a framework that can be used in both electric car systems and energy storage for renewable sources.