Abstract
This paper proposes a highly-accurate state-of-charge (SOC) estimation technique to meet highly dynamic power requirements in electric vehicles (EVs). The proposed technique is a dual auto-covariance least square (DALS) integrated with an extended Kalman filter (EKF) in which the SOC and the model parameters are simultaneously estimated. The proposed technique is used to estimate the SOC of a 12.8-V lithium-ion (Li-ion) battery pack through a Dynamic Stress Test (DST) cycles. Results show a remarkable improvement in the estimation accuracy using the proposed technique.
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