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Nonlinear Observers for Predicting State-of-Charge and State-of-Health of Lead-Acid Batteries for Hybrid-Electric Vehicles
2005
IEEE Transactions on Vehicular Technology
This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence-an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of
doi:10.1109/tvt.2004.842461
fatcat:w322tf2muzeaflzy3d2usuwty4