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Energies 18 06192 by www.autovator is a document available to read on EtoBox.

The paper presents a novel approach for estimating the State-of-Charge (SOC) of Lithium-Ion Batteries (LIBs) by integrating Akima–Savitzky–Golay curve reconstruction with a Bayesian-optimized adaptive Extended Kalman Filter (EKF). This method addresses key challenges in SOC estimation, achieving an average Root Mean Square Error (RMSE) of 2.65% across various temperatures and driving cycles, while enhancing reliability in the critical mid-SOC range. The proposed framework is designed for real-time integrati

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