An Unscented Kalman Filter-Based Robust State of Health Prediction Technique for Lithium Ion Batteries

Electric vehicles (EVs) have emerged as a promising solution for sustainable transportation. The high energy density, long cycle life, and low self-discharge rate of lithium-ion batteries make them an ideal choice EVs. Recently, these been prone to faster decay in life span, leading sudden failure the battery. To avoid uncertainty among EV users with battery failures, robust health monitoring prediction scheme is required management system. In this regard, Unscented Kalman Filter (UKF)-based technique has developed accurate reliable status. UKF approximates nonlinearity using set sigma points propagates via nonlinear function enhance estimation accuracy. Furthermore, UKF-based considers state charge (SOC) internal resistance Here, compared Extended filter (EKF) scheme. robustness EKF-based prognostic techniques were studied under varying initial SOC values. Under abrupt changing conditions, proposed performed effectively terms (SOH) prediction. Accurate SOH determination can help decide when needs be replaced or if adjustments need made extend its life. Ultimately, essential vehicular applications plays pivotal role ensuring sustainability minimizing environmental impacts.