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Date of Award
Electrical and Computer Engineering
Le Y. Wang
Accurate estimation of the state of charge in battery systems is of essential importance for battery system management. Due to nonlinearity, high sensitivity of the inverse mapping from external measurements, and measurement errors, estimation of SOC has remained as a challenging task. This paper introduces an adaptive nonlinear observer design that compensates nonlinearity and achieves better estimation accuracy. A two-time-scale signal processing method is employed to attenuate the effects of measurement noises on SOC estimates. an integrated algorithm is introduced to further identify the internal parameters and initial SOC jointly with a simplified
function of the battery model.
Liu, Lezhang, "Integrated system identification and state-of-charge estimation of battery systems" (2011). Wayne State University Theses. 146.