State-of-charge and state-of-health prediction of lead-acid batteries for hybrid electric vehicles using non-linear observers
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The paper describes the application of state-estimation techniques for the real-time prediction of state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Approaches based on the extended Kalman filter (EKF) are presented to provide correction for offset, drift and state divergence - an unfortunate feature of more traditional coulomb-counting techniques. Experimental results are employed to demonstrate the relative attributes of the proposed methodology
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